Bibliographie réparée

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--- a/biblio.bib
+++ b/biblio.bib
@@ -10,6 +10,106 @@
   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}
+}
+
+@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}
+}
+
+@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}, 
+}
+
+
+
+
+
+
+
 #####################################################""
 #Echelle individuelle 
 
@@ -193,6 +293,11 @@
 
 ################################
 #Définition
+@book{theetete,
+    title={Théétète},
+    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}},
@@ -271,6 +376,52 @@
     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},