path: root/biblio.bib

######################################"
#Background
@BOOK{lecun2019quand,
  title     = "Quand la machine apprend",
  author    = "Le Cun, Yann",
  publisher = "Odile Jacob",
  month     =  oct,
  year      =  2019,
  address   = "Paris, France",
  language  = "fr"
}

#Set
@book{enderton1977elements,
  title={Elements of set theory},
  author={Enderton, Herbert B},
  year={1977},
  publisher={Academic press}
}

#Mesure
@misc{mesure,
    howpublished={\url{https://www-fourier.ujf-grenoble.fr/~edumas/integration.pdf}},
    title={Théorie de la mesure et de l’intégration},
    author={Gallay, Thierry},
    note={Dernier accès: 2024-08-29}
}

@misc{proba,
    title={\url{Intégration, Probabilitées et Processus Aléatoires}},
    howpublished={\url{https://www.imo.universite-paris-saclay.fr/~jean-francois.le-gall/IPPA2.pdf}},
    author={Le Gall, Jean-François},
    note={Dernier accès: 2024-08-29}
}

#Optimisation
@BOOK{ciarlet,
  title    = "Introduction {\`a} l'an{\'a}lyse num{\'e}rique matricielle et
              {\`a} l'optimisation: cours et exercices corrig{\'e}s",
  author   = "Ciarlet, Philippe G",
  year     =  2006,
  language = "fr"
}



#Machine learning

@BOOK{lecun2019quand,
  title     = "Quand la machine apprend",
  author    = "Le Cun, Yann",
  publisher = "Odile Jacob",
  month     =  oct,
  year      =  2019,
  address   = "Paris, France",
  language  = "fr"
}


@article{zou2016finding,
  title={Finding the best classification threshold in imbalanced classification},
  author={Zou, Quan and Xie, Sifa and Lin, Ziyu and Wu, Meihong and Ju, Ying},
  journal={Big Data Research},
  volume={5},
  pages={2--8},
  year={2016},
  publisher={Elsevier}
}


@article{bottou1991stochastic,
  title={Stochastic gradient learning in neural networks},
  author={Bottou, L{\'e}on and others},
  journal={Proceedings of Neuro-N{\i}mes},
  volume={91},
  number={8},
  pages={12},
  year={1991},
  publisher={Nimes}
}

@incollection{bottou2012stochastic,
  title={Stochastic gradient descent tricks},
  author={Bottou, L{\'e}on},
  booktitle={Neural Networks: Tricks of the Trade: Second Edition},
  pages={421--436},
  year={2012},
  publisher={Springer}
}

@article{amari1993back,
  title={Backpropagation and stochastic gradient descent method},
  author={Amari, Shun-ichi},
  journal={Neurocomputing},
  volume={5},
  number={4-5},
  pages={185--196},
  year={1993},
  publisher={Elsevier}
}

@article{kumari2017machine,
  title={Machine learning: A review on binary classification},
  author={Kumari, Roshan and Srivastava, Saurabh Kr},
  journal={International Journal of Computer Applications},
  volume={160},
  number={7},
  year={2017},
  publisher={Foundation of Computer Science}
}
@article{li2020statistical,
  title={Statistical hypothesis testing versus machine learning binary classification: Distinctions and guidelines},
  author={Li, Jingyi Jessica and Tong, Xin},
  journal={Patterns},
  volume={1},
  number={7},
  year={2020},
  publisher={Elsevier}
}
@article{canbek2022ptopi,
  title={PToPI: A comprehensive review, analysis, and knowledge representation of binary classification performance measures/metrics},
  author={Canbek, G{\"u}rol and Taskaya Temizel, Tugba and Sagiroglu, Seref},
  journal={SN Computer Science},
  volume={4},
  number={1},
  pages={13},
  year={2022},
  publisher={Springer}
}

@misc{insee1982parite,
    howpublished={\url{https://www.insee.fr/fr/statistiques/4768237}},
    title={Les cadres : de plus en plus de femmes},
    author={Forment, Virginie and Vidalenc, Joëlle},
    note={Dernier accès: 2024-08-26}
}

@article{chicco2021matthews,
  title={The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation},
  author={Chicco, Davide and T{\"o}tsch, Niklas and Jurman, Giuseppe},
  journal={BioData mining},
  volume={14},
  pages={1--22},
  year={2021},
  publisher={Springer}
}


    








############################################""
#Enjeux
#Securité
#Backdoor
@article{gao2020backdoor,
  title={Backdoor attacks and countermeasures on deep learning: A comprehensive review},
  author={Gao, Yansong and Doan, Bao Gia and Zhang, Zhi and Ma, Siqi and Zhang, Jiliang and Fu, Anmin and Nepal, Surya and Kim, Hyoungshick},
  journal={arXiv preprint arXiv:2007.10760},
  year={2020}
}

@inproceedings{doan2021lira,
  title={Lira: Learnable, imperceptible and robust backdoor attacks},
  author={Doan, Khoa and Lao, Yingjie and Zhao, Weijie and Li, Ping},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={11966--11976},
  year={2021}
}

#Confidentialité 
@misc{discordgpt,
    title={In-Channel Conversation Summaries},
    author={\url{https://support.discord.com/hc/en-us/profiles/2921470028-Buffy}},
    howpublished={\url{https://support.discord.com/hc/en-us/articles/12926016807575-In-Channel-Conversation-Summaries}},
    note={Dernier accès: 2024-08-26}
}






#####################################################""
#Echelle institutionelle 

#Justice prédictive
@article{brayne2015predictive,
  title={Predictive policing},
  author={Brayne, Sarah and Rosenblat, Alex and Boyd, Danah},
  journal={Data \& Civil Rights: A New Era Of Policing And Justice},
  pages={2015--1027},
  year={2015}
}

@misc{soundthinking,
    howpublished={\url{https://www.soundthinking.com/}},
    title={Soundthinking},
    note={Dernier accès: 2024-08-16}
}


@article{
zhiyuan2020limits,
author = {Zhiyuan “Jerry” Lin  and Jongbin Jung  and Sharad Goel  and Jennifer Skeem },
title = {The limits of human predictions of recidivism},
journal = {Science Advances},
volume = {6},
number = {7},
pages = {eaaz0652},
year = {2020},
doi = {10.1126/sciadv.aaz0652},
URL = {https://www.science.org/doi/abs/10.1126/sciadv.aaz0652},
eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.aaz0652},
abstract = {Statistical algorithms can outperform human predictions of recidivism. Dressel and Farid recently found that laypeople were as accurate as statistical algorithms in predicting whether a defendant would reoffend, casting doubt on the value of risk assessment tools in the criminal justice system. We report the results of a replication and extension of Dressel and Farid’s experiment. Under conditions similar to the original study, we found nearly identical results, with humans and algorithms performing comparably. However, algorithms beat humans in the three other datasets we examined. The performance gap between humans and algorithms was particularly pronounced when, in a departure from the original study, participants were not provided with immediate feedback on the accuracy of their responses. Algorithms also outperformed humans when the information provided for predictions included an enriched (versus restricted) set of risk factors. These results suggest that algorithms can outperform human predictions of recidivism in ecologically valid settings.}}

@misc{equivant,
    howpublished={\url{https://www.equivant.com/}},
    title={Equivant},
    note={Dernier accès: 2024-07-24}
}
@article{dildar2021skin,
  title={Skin cancer detection: a review using deep learning techniques},
  author={Dildar, Mehwish and Akram, Shumaila and Irfan, Muhammad and Khan, Hikmat Ullah and Ramzan, Muhammad and Mahmood, Abdur Rehman and Alsaiari, Soliman Ayed and Saeed, Abdul Hakeem M and Alraddadi, Mohammed Olaythah and Mahnashi, Mater Hussen},
  journal={International journal of environmental research and public health},
  volume={18},
  number={10},
  pages={5479},
  year={2021},
  publisher={MDPI}
}


####################################
#Médecine
@article{gulshan2016development,
  title={Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs},
  author={Gulshan, Varun and Peng, Lily and Coram, Marc and Stumpe, Martin C and Wu, Derek and Narayanaswamy, Arunachalam and Venugopalan, Subhashini and Widner, Kasumi and Madams, Tom and Cuadros, Jorge and others},
  journal={jama},
  volume={316},
  number={22},
  pages={2402--2410},
  year={2016},
  publisher={American Medical Association}
}

@article{quinn2022three,
  title={The three ghosts of medical AI: Can the black-box present deliver?},
  author={Quinn, Thomas P and Jacobs, Stephan and Senadeera, Manisha and Le, Vuong and Coghlan, Simon},
  journal={Artificial intelligence in medicine},
  volume={124},
  pages={102158},
  year={2022},
  publisher={Elsevier}
}


##################################
#Recrutement
@misc{fortune500,
    title={Fortune 500},
    howpublished={\url{https://fortune.com/ranking/global500/}},
    note={Dernier accès: 2024-07-24}
}

@article{ore2022opportunities,
  title={Opportunities and risks of artificial intelligence in recruitment and selection},
  author={Ore, Olajide and Sposato, Martin},
  journal={International Journal of Organizational Analysis},
  volume={30},
  number={6},
  pages={1771--1782},
  year={2022},
  publisher={Emerald Publishing Limited}
}

@inproceedings{al2021role,
  title={The role of artificial intelligence in recruitment process decision-making},
  author={Al-Alawi, Adel Ismail and Naureen, Misbah and AlAlawi, Ebtesam Ismaeel and Al-Hadad, Ahmed Abdulla Naser},
  booktitle={2021 International Conference on Decision Aid Sciences and Application (DASA)},
  pages={197--203},
  year={2021},
  organization={IEEE}
}

@misc{segal2021fairnesseyesdatacertifying,
      title={Fairness in the Eyes of the Data: Certifying Machine-Learning Models}, 
      author={Shahar Segal and Yossi Adi and Benny Pinkas and Carsten Baum and Chaya Ganesh and Joseph Keshet},
      year={2021},
      eprint={2009.01534},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2009.01534}, 
}
@article{Hardt2016equality,
  author    = {Moritz Hardt and
               Eric Price and
               Nathan Srebro},
  title     = {Equality of Opportunity in Supervised Learning},
  journal   = {CoRR},
  volume    = {abs/1610.02413},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.02413},
  eprinttype = {arXiv},
  eprint    = {1610.02413},
  timestamp = {Tue, 26 Apr 2022 09:17:17 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HardtPS16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{Dwork2011fairness,
  doi = {10.48550/ARXIV.1104.3913},
  
  url = {https://arxiv.org/abs/1104.3913},
  
  author = {Dwork, Cynthia and Hardt, Moritz and Pitassi, Toniann and Reingold, Omer and Zemel, Rich},
  
  keywords = {Computational Complexity (cs.CC), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Fairness Through Awareness},
  
  publisher = {arXiv},
  
  year = {2011},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}



@inproceedings{10.1145/3278721.3278779,
author = {Zhang, Brian Hu and Lemoine, Blake and Mitchell, Margaret},
title = {Mitigating Unwanted Biases with Adversarial Learning},
year = {2018},
isbn = {9781450360128},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3278721.3278779},
doi = {10.1145/3278721.3278779},
abstract = {Machine learning is a tool for building models that accurately represent input training data. When undesired biases concerning demographic groups are in the training data, well-trained models will reflect those biases. We present a framework for mitigating such biases by including a variable for the group of interest and simultaneously learning a predictor and an adversary. The input to the network X, here text or census data, produces a prediction Y, such as an analogy completion or income bracket, while the adversary tries to model a protected variable Z, here gender or zip code. The objective is to maximize the predictor's ability to predict Y while minimizing the adversary's ability to predict Z. Applied to analogy completion, this method results in accurate predictions that exhibit less evidence of stereotyping Z. When applied to a classification task using the UCI Adult (Census) Dataset, it results in a predictive model that does not lose much accuracy while achieving very close to equality of odds (Hardt, et al., 2016). The method is flexible and applicable to multiple definitions of fairness as well as a wide range of gradient-based learning models, including both regression and classification tasks.},
booktitle = {Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society},
pages = {335–340},
numpages = {6},
keywords = {multi-task learning, debiasing, adversarial learning, unbiasing},
location = {New Orleans, LA, USA},
series = {AIES '18}
}








#####################################################""
#Echelle individuelle 

@misc{applewatch,
    title={WatchOS 11 brings powerful health and fitness insights, and even more personalization and connectivity },
    howpublished={\url{https://www.apple.com/newsroom/2024/06/watchos-11-brings-powerful-health-and-fitness-insights/}},
    note={Dernier accès: 2024-07-24}
}

@inproceedings{barthelemy:hal-01837361,
  TITLE = {{Pl@ntNet, une plate-forme innovante d'agr{\'e}gation et partage d'observations botaniques}},
  AUTHOR = {Barth{\'e}l{\'e}my, Daniel and Boujemaa, Nozha and Molino, Jean-Fran{\c c}ois and Joly, Alexis and Go{\"e}au, Herv{\'e} and Baki{\'c}, Vera and Selmi, Souheil and Champ, Julien and Carre, Jennifer and Chouet, Mathias and Perronnet, Aur{\'e}lien and Vignau, Christelle and Dufour-Kowalski, Samuel and Affouard, Antoine and Barbe, Julien and Bonnet, Pierre},
  URL = {https://hal.science/hal-01837361},
  BOOKTITLE = {{International Conference ‘Botanists of the Twenty-first Century'}},
  ADDRESS = {Paris, France},
  ORGANIZATION = {{UNESCO}},
  HAL_LOCAL_REFERENCE = {DEVMP},
  EDITOR = {No{\"e}line R. Rakotoarisoa and Stephen Blackmore and Bernard Riera},
  PAGES = {191-197},
  YEAR = {2014},
  MONTH = Sep,
  KEYWORDS = {Pl@ntNet ; Botany ; Plateforme participative ; Observations botaniques},
  PDF = {https://hal.science/hal-01837361/file/DB_etal_plantnet_plateforme_2016_1.pdf},
  HAL_ID = {hal-01837361},
  HAL_VERSION = {v1},
}

@misc{plantnet,
    title={Pl@ntNet},
    howpublished={\url{https://identify.plantnet.org/}},
    note={Dernier accès: 2024-07-24}
}


@article{dunn2018wearables,
  title={Wearables and the medical revolution},
  author={Dunn, Jessilyn and Runge, Ryan and Snyder, Michael},
  journal={Personalized medicine},
  volume={15},
  number={5},
  pages={429--448},
  year={2018},
  publisher={Taylor \& Francis}
}

########################################"
#Intéret pour l'IA
#Google trend 
@misc{gtrend,
    title={Google trend Intelligence Artificielle},
    howpublished={\url{https://trends.google.com/trends/explore?date=all&geo=FR&q=intelligence%20artificielle&hl=en-US}},
    note={Dernier accès: 2024-07-24}
}

####################################""
#Stratégie AI de la France
@misc{france2030,
    title={France 2030},
    howpublished={\url{https://www.info.gouv.fr/grand-dossier/france-2030}},
    note={Dernier accès: 2024-07-24}
}

@misc{stratfr,
    title={La stratégie nationale pour l'intelligence artificielle},
    howpublished={\url{https://www.entreprises.gouv.fr/fr/numerique/enjeux/la-strategie-nationale-pour-l-ia}},
    note={Dernier accès: 2024-07-24}
}

@book{villani2018donner,
  TITLE = {{Donner un sens {\`a} l'intelligence artificielle}},
  AUTHOR = {Villani, C{\'e}dric and Schoenauer, Marc and Bonnet, Yann and Berthet, Charly and Cornut, Anne-Charlotte and Levin, Fran{\c c}ois and Rondepierre, Bertrand},
  URL = {https://inria.hal.science/hal-01967551},
  PUBLISHER = {{Mission Villani sur l'intelligence artificielle}},
  YEAR = {2018},
  MONTH = Mar,
  PDF = {https://inria.hal.science/hal-01967551/file/9782111457089_Rapport_Villani_accessible.pdf},
  HAL_ID = {hal-01967551},
  HAL_VERSION = {v1},
}

%%%%%%%%%%%CLIMATE CHANGE BACKGROUND
@article{barnes2019viewing,
  title={Viewing forced climate patterns through an AI lens},
  author={Barnes, Elizabeth A and Hurrell, James W and Ebert-Uphoff, Imme and Anderson, Chuck and Anderson, David},
  journal={Geophysical Research Letters},
  volume={46},
  number={22},
  pages={13389--13398},
  year={2019},
  publisher={Wiley Online Library}
}

@article{slater2023hybrid,
  title={Hybrid forecasting: blending climate predictions with AI models},
  author={Slater, Louise J and Arnal, Louise and Boucher, Marie-Am{\'e}lie and Chang, Annie Y-Y and Moulds, Simon and Murphy, Conor and Nearing, Grey and Shalev, Guy and Shen, Chaopeng and Speight, Linda and others},
  journal={Hydrology and earth system sciences},
  volume={27},
  number={9},
  pages={1865--1889},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

%%%%%%%%%%%%ENERGY BACKGROUND
@article{jin2020energy,
  title={Energy and AI},
  author={Jin, Donghan and Ocone, Raffaella and Jiao, Kui and Xuan, Jin},
  journal={Energy and AI},
  volume={1},
  pages={100002},
  year={2020},
  publisher={Elsevier}
}

@article{kumar2020distributed,
  title={Distributed energy resources and the application of AI, IoT, and blockchain in smart grids},
  author={Kumar, Nallapaneni Manoj and Chand, Aneesh A and Malvoni, Maria and Prasad, Kushal A and Mamun, Kabir A and Islam, FR and Chopra, Shauhrat S},
  journal={Energies},
  volume={13},
  number={21},
  pages={5739},
  year={2020},
  publisher={MDPI}
}

@article{kumari2020blockchain,
  title={Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions},
  author={Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
  journal={Journal of Parallel and Distributed Computing},
  volume={143},
  pages={148--166},
  year={2020},
  publisher={Elsevier}
}

@article{ngarambe2020use,
  title={The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls},
  author={Ngarambe, Jack and Yun, Geun Young and Santamouris, Mat},
  journal={Energy and Buildings},
  volume={211},
  pages={109807},
  year={2020},
  publisher={Elsevier}
}


%%%%%OPEN AI 

@misc{openaibfm,
    title={OpenAI, cette société qui révolutionne l'intelligence artificielle},
    howpublished={\url{https://www.bfmtv.com/tech/intelligence-artificielle/open-ai-cette-societe-qui-revolutionne-l-intelligence-artificielle_DN-202311200564.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaiinter,
    title={Intelligence artificielle : pourquoi Sam Altman, créateur de ChatGPT, a été débarqué d'OpenAI},
    howpublished={\url{https://www.radiofrance.fr/franceinter/ce-que-l-on-sait-du-renvoi-de-sam-altman-patron-d-openai-et-createur-de-chatgpt-5672369}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaint,
    title={OpenAI Says It Has Begun Training a New Flagship A.I. Model},
    howpublished={\url{https://www.nytimes.com/2024/05/28/technology/openai-gpt4-new-model.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaibig,
    title={ChatGPT sets record for fastest-growing user base - analyst note},
    howpublished={\url{https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/}},
    note={Dernier accès: 2024-07-24}
}

@misc{gptjournal,
    title={ChatGPT : le quotidien Le Monde signe un partenariat avec OpenAI, une première en France},
    howpublished={\url{https://www.radiofrance.fr/franceinter/podcasts/l-info-de-france-inter/les-doc-france-inter-du-jeudi-14-mars-3-7619379}},
    note={Dernier accès: 2024-07-24}
}

##################################
#Chine surveillance de la population

@article{beraja2023ai,
  title={AI-tocracy},
  author={Beraja, Martin and Kao, Andrew and Yang, David Y and Yuchtman, Noam},
  journal={The Quarterly Journal of Economics},
  volume={138},
  number={3},
  pages={1349--1402},
  year={2023},
  publisher={Oxford University Press}
}

################################
#Définition
@article{baum2017survey,
  title={A survey of artificial general intelligence projects for ethics, risk, and policy},
  author={Baum, Seth},
  journal={Global Catastrophic Risk Institute Working Paper},
  pages={17--1},
  year={2017}
}

@inproceedings{kuppa2021towards,
  title={Towards improving privacy of synthetic datasets},
  author={Kuppa, Aditya and Aouad, Lamine and Le-Khac, Nhien-An},
  booktitle={Annual Privacy Forum},
  pages={106--119},
  year={2021},
  organization={Springer}
}
@inproceedings{arpit2017closer,
  title={A closer look at memorization in deep networks},
  author={Arpit, Devansh and Jastrzebski, Stanislaw and Ballas, Nicolas and Krueger, David and Bengio, Emmanuel and Kanwal, Maxinder S and Maharaj, Tegan and Fischer, Asja and Courville, Aaron and Bengio, Yoshua and others},
  booktitle={International conference on machine learning},
  pages={233--242},
  year={2017},
  organization={PMLR}
}
@inproceedings{feldman2020does,
  title={Does learning require memorization? a short tale about a long tail},
  author={Feldman, Vitaly},
  booktitle={Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing},
  pages={954--959},
  year={2020}
}

@book{theetete,
    title={Théétète},
    author={Platon},
    year={300 av. JC}
}
@book{caverne,
    title={La République},
    author={Platon},
    year={300 av. JC}
}
@misc{dartmouth,
    title={Dartmouth summer research project on artificiale intelligence},
    howpublished={\url{https://raysolomonoff.com/dartmouth/boxa/dart564props.pdf}},
    author={
        McCarthy, John and Minsky, Marvin and Rochester Nathaniel and Shannon, Claude
    },
    note={Dernier accès: 2024-08-05}
}

@misc{banIA,
    title={En 2024, bannissons les termes "intelligence artificielle"},
    howpublished={\url{https://www.radiofrance.fr/franceculture/podcasts/le-biais-d-aurelie-jean/le-biais-d-aurelie-jean-chronique-du-mardi-02-janvier-2024-9653995}},
    author={Jean, Aurélie},
    note={Dernier accès: 2024-08-05}
}

@misc{gnuAI,
    title={Words to Avoid (or Use with Care) Because They Are Loaded or Confusing.},
    howpublished={\url{https://www.gnu.org/philosophy/words-to-avoid.html#ArtificialIntelligence}},
    note={Dernier accès: 2024-08-05}
}
@book{dico-int,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{http://www.dictionnaire-academie.fr/article/A9I1608}, Dernier accès: 2024-08-05}
}
@book{dico-art,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{http://www.dictionnaire-academie.fr/article/A9A2706},Dernier accès: 2024-08-05}
}
@book{dico-con,
    title={Dictionaire de l'Académie francaise, 9° édition},
    note={\url{https://www.dictionnaire-academie.fr/article/A9C3633},Dernier accès: 2024-08-16}
}
@misc{underscore,
    title={Cette nouvelle IA est bluffante},
    author={Chaîne Youtube Underscore},
    year={2024},
    howpublished={\url{https://www.youtube.com/watch?v=QUr93cD2ZUs}},
    note={Dernier accès: 2024-08-05}
}
@misc{grep,
    title={grep},
    howpublished={\url{https://www.gnu.org/software/grep/manual/grep.html}},
    note={Dernier accès: 2024-08-05}
}
@misc{ocrad,
    title={Ocrad},
    howpublished={\url{https://www.gnu.org/software/ocrad/ocrad.html}},
    note={Dernier accès: 2024-08-05}
}

@misc{aiact,
    howpublished={\url{https://eur-lex.europa.eu/eli/reg/2024/1689/oj}},

    note={Dernier accès: 2024-09-02},
    title={Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (Text with EEA relevance)}
}



#########################################
#Philosophie

@misc{siri,
    title={Siri},
    author={Apple},
    howpublished={\url{https://www.apple.com/siri/}},
    note={Dernier accès: 2024-08-26}
}

@misc{discord,
    title={Messagerie Discord},
    author={Discord},
    howpublished={\url{https://discord.com/}},
    note={Dernier accès: 2024-08-26}
}

@misc{googleai,
    title={Google assistant},
    author={Google},
    howpublished={\url{https://assistant.google.com/}},
    note={Dernier accès: 2024-08-26}
}

@misc{aaigpt,
    title={Apple inteligence ChatGPT},
    author={Apple},
    howpublished={\url{https://www.apple.com/newsroom/2024/06/introducing-apple-intelligence-for-iphone-ipad-and-mac/}},
    note={Dernier accès: 2024-08-26}
}



@BOOK{Freud2010-qq,
  title     = "Le moi et le {\c c}a",
  author    = "Freud, Sigmund",
  publisher = "Payot",
  year      =  2010,
  language  = "fr",
  note={Das Ich und das Es, 1923. Traduction Jean Laplanche}
}

@article{waters2014grade,
  title={Grade: Machine learning support for graduate admissions},
  author={Waters, Austin and Miikkulainen, Risto},
  journal={Ai Magazine},
  volume={35},
  number={1},
  pages={64--64},
  year={2014}
}


@book{rousseau1762contrat,
    title={Du contrat social ou Principes du droit politique},
    author={Rousseau, Jean-Jeacques},
    year={1762}
}

@BOOK{Poundstone1993-jr,
  title     = "Prisoner's Dilemma",
  author    = "Poundstone, William",
  publisher = "Anchor Books",
  month     =  jan,
  year      =  1993,
  address   = "New York, NY"
}


@article{wang2023not,
  title={Do-not-answer: A dataset for evaluating safeguards in llms},
  author={Wang, Yuxia and Li, Haonan and Han, Xudong and Nakov, Preslav and Baldwin, Timothy},
  journal={arXiv preprint arXiv:2308.13387},
  year={2023}
}

@article{bergeaud2023teletravail,
  title={T{\'e}l{\'e}travail et productivit{\'e} avant, pendant et apr{\`e}s la pand{\'e}mie de Covid-19/Telework and Productivity Before, During and After the COVID-19 Crisis},
  author={Bergeaud, Antonin and Cette, Gilbert and Drapala, Simon},
  journal={Economie et Statistique},
  volume={539},
  number={1},
  pages={77--93},
  year={2023},
  publisher={Pers{\'e}e-Portail des revues scientifiques en SHS}
}

@misc{metaverse,
    title={What is the metaverse?},
    author={Meta},
    howpublished={\url{https://about.meta.com/what-is-the-metaverse/}},
    note={Dernier accès: 2024-08-22}
}

@misc{applevision,
    title={Apple vision pro},
    howpublished={\url{https://www.apple.com/apple-vision-pro/}},
    author={Apple},
    note={Dernier accès: 2024-08-22}



@article{johnson2017ai,
  title={AI anxiety},
  author={Johnson, Deborah G and Verdicchio, Mario},
  journal={Journal of the Association for Information Science and Technology},
  volume={68},
  number={9},
  pages={2267--2270},
  year={2017},
  publisher={Wiley Online Library}
}

@misc{afi100,
    title={100 YEARS...100 MOVIES},
    author={AMERICAN FILM INSTITUTE},
    howpublished={\url{https://www.afi.com/afis-100-years-100-movies-10th-anniversary-edition/}},
    note={Dernier accès: 2024-08-21}
}


@article{bernays1928manipulating,
author = {Bernays, Edward L.},
title = {Manipulating Public Opinion: The Why and The How},
journal = {American Journal of Sociology},
volume = {33},
number = {6},
pages = {958-971},
year = {1928},
doi = {10.1086/214599},
URL = { 
    
        https://doi.org/10.1086/214599
},
eprint = { 
    
        https://doi.org/10.1086/214599
}
,
    abstract = { Public opinion, narrowly defined, is the thought of a society at a given time toward a given object; broadly conceived, it is the power of the group to sway the larger public in its attitude. Public opinion can be manipulated, but in teaching the public how to ask for what it wants the manipulator is safeguarding the public against his own possible aggressiveness. The method of the experimental psychologist is not as effective in the study of public opinion in the broad sense as is that of introspective psychology. To create and to change public opinion it is necessary to understand human motives, to know what special interests are represented by a given population, and to realize the function and limitations of the physical organs of approach to the public, such as the radio, the platform, the movie, the letter, the newspaper, etc. If the general principles of swaying public opinion are understood, a technique can be developed which, with the correct appraisal of the specific problem and the specific audience, can and has been used effectively in such widely different situations as changing the attitudes of whites toward Negroes in America, changing the buying habits of American women from felt hats to velvet, silk, and straw hats, changing the impression which the American electorate has of its President, introducing new musical instruments, and a variety of others. Group adherence is essential in changing the attitudes of the public. Authoritative and influential groups may become important channels of reaching the larger public. Ideas and situations must be made impressive and dramatic in order to overcome the inertia of established traditions and prejudices. }
}



@article{fearing1947influence,
author = {Franklin Fearing},
title ={Influence of the Movies on Attitudes and Behavior},

journal = {The ANNALS of the American Academy of Political and Social Science},
volume = {254},
number = {1},
pages = {70-79},
year = {1947},
doi = {10.1177/000271624725400112},

URL = { 
        https://doi.org/10.1177/000271624725400112
},
eprint = { 
        https://doi.org/10.1177/000271624725400112
}
}
@misc{roko,
    title={Solutions to the Altruist's burden: the Quantum Billionaire Trick},
    year={2010},
    author={Roko},
    howpublished={\url{https://rationalwiki.org/wiki/Roko%27s_basilisk/Original_post#Solutions_to_the_Altruist.27s_burden:_the_Quantum_Billionaire_Trick}},
    note={Dernier accès: 2024-08-22}
}
@misc{rokowiki,
    title={Roko's basilisk},
    howpublished={\url{https://old-wiki.lesswrong.com/wiki/Roko%27s_basilisk#Roko's_post}},
    note={Dernier accès: 2024-08-22}
}
@misc{slate,
    title={The Most Terrifying Thought Experiment of All Time},
    author={Auerbach, David},
    howpublished={\url{https://slate.com/technology/2014/07/rokos-basilisk-the-most-terrifying-thought-experiment-of-all-time.html}},
    note={Dernier accès: 2024-08-22}
}
@misc{rokomisc,
    title={A few misconceptions surrounding Roko's basilisk},
    author={Bensinger, Rob}
    howpublished={\url{https://www.lesswrong.com/posts/WBJZoeJypcNRmsdHx/a-few-misconceptions-surrounding-roko-s-basilisk}},
    note={Dernier accès: 2024-08-22}
}

@article{Singler_2018, title={Roko’s Basilisk or Pascal’s? Thinking of Singularity Thought Experiments as Implicit Religion}, volume={20},  url={https://journal.equinoxpub.com/IR/article/view/3226},  DOI={10.1558/imre.35900}, abstractNote={In 2010 a thought experiment speculating on the motivations and aims of a potential superintelligent Artificial Intelligence, sometimes known as the ‘Singularity’, caused uproar and anxiety on the forum board where it was initially posted. This paper considers that thought experiment’s debt to older forms of religious argument, the reactions from among the community, and how expectations about the Singularity as a being with agency can be considered to be an example of implicit religion. This is significant as the thought experiment appeared in a field of research, AI, considered by many to be secular due to its technological focus. The communities under discussion also explicitly express their aim of ‘perfecting’ human rationality, and place that ability in opposition to ‘religion’ as a derided object and the aims of ‘Goddists’ in general. This tension between overt atheism and secular communities’ return to religious tropes and narratives is relevant for the wider study of religion in the contemporary era.}, number={3}, journal={Implicit Religion}, author={Singler, Beth}, year={2018}, month={May}, pages={279–297} }

@misc{matrix,
    title={The Matrix},
    author={Wachowski and Silver and Pope},
    year={1999}
}

@misc{her,
    title={Her},
    author={Jonze, Spike},
    year={2013}
}

@misc{johansson,
    title={Scarlett Johansson’s Statement About Her Interactions With Sam Altman},
    howpublished={\url{https://www.nytimes.com/2024/05/20/technology/scarlett-johansson-openai-statement.html}},
    note={Dernier accès: 2024-08-21}
}


@book{bicentenaire,
    title={The Bicentennial Man},
    author={Asimov,Isaac},
    year={1976},
}


@misc{avenger,
    title={Avengers: Age of Ultron},
    author={ Whedon, Joss and Feige, Kevin},
    year={2015},
    note={Based on the comics by Stan Lee and Jack Kirby}
}
@misc{terminator,
    title={The Terminator},
    author={Cameron, James and Hurd, Gale Anne},
    year={1999}
}

@misc{2001odyssey,
    title={2001: A space odyssey},
    author={Kubrick, Stanley and Clarke, Arthur C. },
    year={1968}
}
@misc{futurama,
    title={Futurama},
    author={Groening, Matt},
    year={2003}
}

@misc{wargames,
    title={War games},
    author={Badham, John and Lasker, Lawrence and Parkes, Walter F. and Schneider,Harold},
    year={1983}
}

@book{assimovIrobot,
    title={I, Robot},
    year={1950},
    author={Isaac Asimov}
}

@book{cornu,
    title={Vocabulaire juridique},
    author={Cornu, Gérard},
    year={2014},
    note={Dixième édition}
}

@article{MARAKAS2000719,
title = {A theoretical model of differential social attributions toward computing technology: when the metaphor becomes the model},
journal = {International Journal of Human-Computer Studies},
volume = {52},
number = {4},
pages = {719-750},
year = {2000},
issn = {1071-5819},
doi = {https://doi.org/10.1006/ijhc.1999.0348},
url = {https://www.sciencedirect.com/science/article/pii/S1071581999903488},
author = {GEORGE M. MARAKAS and RICHARD D. JOHNSON and JONATHAN W. PALMER},
keywords = {anthropomorphism, symbolic computing, social acts, laws of control, computer self-efficiency.},
abstract = {This paper explores the use of metaphorical personification (anthropomorphism) as an aid to describing and understanding the complexities of computing technologies. This common and seemingly intuitive practice (it “reads”, “writes”, “thinks”, “is friendly”, “catches and transmits viruses”, etc.) has become the standard by which we formulate our daily communications, and often our formal training mechanisms, with regard to the technology. Both anecdotal and empirical sources have reported numerous scenarios in which computers have played a noticeably social role, thus being positioned more as a social actor than as a machine or “neutral tool.” In these accounts, human behavior has ranged from making social reference to the device (“It's really much smarter than me,”), to more overt social interactions including conversational interplay and display of common human emotions in response to an interaction. Drawing from behavioral psychology and attribution theory, a theoretical model of the phenomenon is offered from which several propositions are advanced regarding the nature of the behavior, positive and negative implications associated with extended use of this metaphor, and recommendations for research into this ubiquitous social phenomena. … I have encountered these situations before, and in every case they were the result of human error. -HAL 9000 from Arthur C. Clarke's 2001: A Space Odyssey}
}


@article{searle1980minds,
  title={Minds, brains, and programs},
  author={Searle, John R},
  journal={Behavioral and brain sciences},
  volume={3},
  number={3},
  pages={417--424},
  year={1980},
  publisher={Cambridge University Press}
}

@misc{oms,
    title={Rapport de l'Organisation Mondiale de la Santé},
    howpublished={\url{https://www.who.int/fr/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use}},
    author={OMS},
    year={2021}
}


###############################################
#Synthetic 
@misc{carlini2022membershipinferenceattacksprinciples,
      title={Membership Inference Attacks From First Principles}, 
      author={Nicholas Carlini and Steve Chien and Milad Nasr and Shuang Song and Andreas Terzis and Florian Tramer},
      year={2022},
      eprint={2112.03570},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2112.03570}, 
}

@article{brayne2015predictive,
  title={Predictive policing},
  author={Brayne, Sarah and Rosenblat, Alex and Boyd, Danah},
  journal={Data \& Civil Rights: A New Era Of Policing And Justice},
  pages={2015--1027},
  year={2015}
}

@inproceedings{barthelemy:hal-01837361,
  TITLE = {{Pl@ntNet, une plate-forme innovante d'agr{\'e}gation et partage d'observations botaniques}},
  AUTHOR = {Barth{\'e}l{\'e}my, Daniel and Boujemaa, Nozha and Molino, Jean-Fran{\c c}ois and Joly, Alexis and Go{\"e}au, Herv{\'e} and Baki{\'c}, Vera and Selmi, Souheil and Champ, Julien and Carre, Jennifer and Chouet, Mathias and Perronnet, Aur{\'e}lien and Vignau, Christelle and Dufour-Kowalski, Samuel and Affouard, Antoine and Barbe, Julien and Bonnet, Pierre},
  URL = {https://hal.science/hal-01837361},
  BOOKTITLE = {{International Conference ‘Botanists of the Twenty-first Century'}},
  ADDRESS = {Paris, France},
  ORGANIZATION = {{UNESCO}},
  HAL_LOCAL_REFERENCE = {DEVMP},
  EDITOR = {No{\"e}line R. Rakotoarisoa and Stephen Blackmore and Bernard Riera},
  PAGES = {191-197},
  YEAR = {2014},
  MONTH = Sep,
  KEYWORDS = {Pl@ntNet ; Botany ; Plateforme participative ; Observations botaniques},
  PDF = {https://hal.science/hal-01837361/file/DB_etal_plantnet_plateforme_2016_1.pdf},
  HAL_ID = {hal-01837361},
  HAL_VERSION = {v1},
}

@misc{plantnet,
    title={Pl@ntNet},
    howpublished={\url{https://identify.plantnet.org/}},
    note={Dernier accès: 2024-07-24}
}


@article{dunn2018wearables,
  title={Wearables and the medical revolution},
  author={Dunn, Jessilyn and Runge, Ryan and Snyder, Michael},
  journal={Personalized medicine},
  volume={15},
  number={5},
  pages={429--448},
  year={2018},
  publisher={Taylor \& Francis}
}

@misc{gtrend,
    title={Google trend Intelligence Artificielle},
    howpublished={\url{https://trends.google.com/trends/explore?date=all&geo=FR&q=intelligence%20artificielle&hl=en-US}},
    note={Dernier accès: 2024-07-24}
}

@misc{france2030,
    title={France 2030},
    howpublished={\url{https://www.info.gouv.fr/grand-dossier/france-2030}},
    note={Dernier accès: 2024-07-24}
}

@misc{stratfr,
    title={La stratégie nationale pour l'intelligence artificielle},
    howpublished={\url{https://www.entreprises.gouv.fr/fr/numerique/enjeux/la-strategie-nationale-pour-l-ia}},
    note={Dernier accès: 2024-07-24}
}

@misc{applewatch,
    title={WatchOS 11 brings powerful health and fitness insights, and even more personalization and connectivity },
    howpublished={\url{https://www.apple.com/newsroom/2024/06/watchos-11-brings-powerful-health-and-fitness-insights/}},
    note={Dernier accès: 2024-07-24}
}

%%%%%%%%%%%CLIMATE CHANGE BACKGROUND
@article{barnes2019viewing,
  title={Viewing forced climate patterns through an AI lens},
  author={Barnes, Elizabeth A and Hurrell, James W and Ebert-Uphoff, Imme and Anderson, Chuck and Anderson, David},
  journal={Geophysical Research Letters},
  volume={46},
  number={22},
  pages={13389--13398},
  year={2019},
  publisher={Wiley Online Library}
}

@article{slater2023hybrid,
  title={Hybrid forecasting: blending climate predictions with AI models},
  author={Slater, Louise J and Arnal, Louise and Boucher, Marie-Am{\'e}lie and Chang, Annie Y-Y and Moulds, Simon and Murphy, Conor and Nearing, Grey and Shalev, Guy and Shen, Chaopeng and Speight, Linda and others},
  journal={Hydrology and earth system sciences},
  volume={27},
  number={9},
  pages={1865--1889},
  year={2023},
  publisher={Copernicus Publications G{\"o}ttingen, Germany}
}

%%%%%%%%%%%%ENERGY BACKGROUND
@article{jin2020energy,
  title={Energy and AI},
  author={Jin, Donghan and Ocone, Raffaella and Jiao, Kui and Xuan, Jin},
  journal={Energy and AI},
  volume={1},
  pages={100002},
  year={2020},
  publisher={Elsevier}
}

@article{kumar2020distributed,
  title={Distributed energy resources and the application of AI, IoT, and blockchain in smart grids},
  author={Kumar, Nallapaneni Manoj and Chand, Aneesh A and Malvoni, Maria and Prasad, Kushal A and Mamun, Kabir A and Islam, FR and Chopra, Shauhrat S},
  journal={Energies},
  volume={13},
  number={21},
  pages={5739},
  year={2020},
  publisher={MDPI}
}

@article{kumari2020blockchain,
  title={Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions},
  author={Kumari, Aparna and Gupta, Rajesh and Tanwar, Sudeep and Kumar, Neeraj},
  journal={Journal of Parallel and Distributed Computing},
  volume={143},
  pages={148--166},
  year={2020},
  publisher={Elsevier}
}

@article{ngarambe2020use,
  title={The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: Energy implications of AI-based thermal comfort controls},
  author={Ngarambe, Jack and Yun, Geun Young and Santamouris, Mat},
  journal={Energy and Buildings},
  volume={211},
  pages={109807},
  year={2020},
  publisher={Elsevier}
}


%%%%%OPEN AI 

@misc{openaibfm,
    title={OpenAI, cette société qui révolutionne l'intelligence artificielle},
    howpublished={\url{https://www.bfmtv.com/tech/intelligence-artificielle/open-ai-cette-societe-qui-revolutionne-l-intelligence-artificielle_DN-202311200564.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaiinter,
    title={Intelligence artificielle : pourquoi Sam Altman, créateur de ChatGPT, a été débarqué d'OpenAI},
    howpublished={\url{https://www.radiofrance.fr/franceinter/ce-que-l-on-sait-du-renvoi-de-sam-altman-patron-d-openai-et-createur-de-chatgpt-5672369}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaint,
    title={OpenAI Says It Has Begun Training a New Flagship A.I. Model},
    howpublished={\url{https://www.nytimes.com/2024/05/28/technology/openai-gpt4-new-model.html}},
    note={Dernier accès: 2024-07-24}
}

@misc{openaibg,
    title={ChatGPT sets record for fastest-growing user base - analyst note},
    howpublished={\url{https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/}},
    note={Dernier accès: 2024-07-24}
}

@misc{gptjournal,
    title={ChatGPT : le quotidien Le Monde signe un partenariat avec OpenAI, une première en France},
    howpublished={\url{https://www.radiofrance.fr/franceinter/podcasts/l-info-de-france-inter/les-doc-france-inter-du-jeudi-14-mars-3-7619379}},
    note={Dernier accès: 2024-07-24}
}

@article{beraja2023ai,
  title={AI-tocracy},
  author={Beraja, Martin and Kao, Andrew and Yang, David Y and Yuchtman, Noam},
  journal={The Quarterly Journal of Economics},
  volume={138},
  number={3},
  pages={1349--1402},
  year={2023},
  publisher={Oxford University Press}
}





@misc{song2020overlearning,
      title={Overlearning Reveals Sensitive Attributes}, 
      author={Congzheng Song and Vitaly Shmatikov},
      year={2020},
      eprint={1905.11742},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{EO,
  author    = {Moritz Hardt and
               Eric Price and
               Nathan Srebro},
  title     = {Equality of Opportunity in Supervised Learning},
  journal   = {CoRR},
  volume    = {abs/1610.02413},
  year      = {2016},
  url       = {http://arxiv.org/abs/1610.02413},
  eprinttype = {arXiv},
  eprint    = {1610.02413},
  timestamp = {Tue, 26 Apr 2022 09:17:17 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/HardtPS16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{hawkins2004problem,
  title={The problem of overfitting},
  author={Hawkins, Douglas M},
  journal={Journal of chemical information and computer sciences},
  volume={44},
  number={1},
  pages={1--12},
  year={2004},
  publisher={ACS Publications}
}

@misc{yeom,
      title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting}, 
      author={Samuel Yeom and Irene Giacomelli and Matt Fredrikson and Somesh Jha},
      year={2018},
      eprint={1709.01604},
      archivePrefix={arXiv},
      primaryClass={cs.CR}
}

@misc{vgg16,
      title={Very Deep Convolutional Networks for Large-Scale Image Recognition}, 
      author={Karen Simonyan and Andrew Zisserman},
      year={2015},
      eprint={1409.1556},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/1409.1556}, 
}
@misc{CGAN,
      title={Conditional Generative Adversarial Nets}, 
      author={Mehdi Mirza and Simon Osindero},
      year={2014},
      eprint={1411.1784},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1411.1784}, 
}
@ARTICLE{cnn,

  author={Rawat, Waseem and Wang, Zenghui},

  journal={Neural Computation}, 

  title={Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review}, 

  year={2017},

  volume={29},

  number={9},

  pages={2352-2449},

  keywords={},

  doi={10.1162/neco_a_00990}}

@misc{dcgan,
      title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks}, 
      author={Alec Radford and Luke Metz and Soumith Chintala},
      year={2016},
      eprint={1511.06434},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1511.06434}
}
@inproceedings{gan,
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
title = {Generative adversarial nets},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {2672–2680},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}
@misc{ctgan,
      title={Modeling Tabular data using Conditional GAN}, 
      author={Lei Xu and Maria Skoularidou and Alfredo Cuesta-Infante and Kalyan Veeramachaneni},
      year={2019},
      eprint={1907.00503},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1907.00503}, 
}
@article{bellovin2019privacy,
  title={Privacy and synthetic datasets},
  author={Bellovin, Steven M and Dutta, Preetam K and Reitinger, Nathan},
  journal={Stan. Tech. L. Rev.},
  volume={22},
  pages={1},
  year={2019},
  publisher={HeinOnline}
}

@inproceedings{ping2017datasynthesizer,
  title={Datasynthesizer: Privacy-preserving synthetic datasets},
  author={Ping, Haoyue and Stoyanovich, Julia and Howe, Bill},
  booktitle={Proceedings of the 29th International Conference on Scientific and Statistical Database Management},
  pages={1--5},
  year={2017}
}

@inproceedings{kuppa2021towards,
  title={Towards improving privacy of synthetic datasets},
  author={Kuppa, Aditya and Aouad, Lamine and Le-Khac, Nhien-An},
  booktitle={Annual Privacy Forum},
  pages={106--119},
  year={2021},
  organization={Springer}
}

@article{tai2023user,
  title={User-Driven Synthetic Dataset Generation with Quantifiable Differential Privacy},
  author={Tai, Bo-Chen and Tsou, Yao-Tung and Li, Szu-Chuang and Huang, Yennun and Tsai, Pei-Yuan and Tsai, Yu-Cheng},
  journal={IEEE Transactions on Services Computing},
  year={2023},
  publisher={IEEE}
}
@article{stadler2020synthetic,
  title={Synthetic data-A privacy mirage},
  author={Stadler, Theresa and Oprisanu, Bristena and Troncoso, Carmela},
  journal={arXiv preprint arXiv:2011.07018},
  year={2020},
  publisher={Nov}
}

@inproceedings{jordon2021hide,
  title={Hide-and-seek privacy challenge: Synthetic data generation vs. patient re-identification},
  author={Jordon, James and Jarrett, Daniel and Saveliev, Evgeny and Yoon, Jinsung and Elbers, Paul and Thoral, Patrick and Ercole, Ari and Zhang, Cheng and Belgrave, Danielle and van der Schaar, Mihaela},
  booktitle={NeurIPS 2020 Competition and Demonstration Track},
  pages={206--215},
  year={2021},
  organization={PMLR}
}

@inproceedings{abadi2016deep,
  title={Deep learning with differential privacy},
  author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
  booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
  pages={308--318},
  year={2016}
}

@inproceedings{shokri2017membership,
  title={Membership inference attacks against machine learning models},
  author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly},
  booktitle={2017 IEEE symposium on security and privacy (SP)},
  pages={3--18},
  year={2017},
  organization={IEEE}
}

@article{ding2021retiring,
  title={Retiring Adult: New Datasets for Fair Machine Learning},
  author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  year={2021}
}

@inproceedings{zhifei2017cvpr,
  title={Age Progression/Regression by Conditional Adversarial Autoencoder},
  author={Zhang, Zhifei and Song, Yang and Qi, Hairong},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  organization={IEEE}
}