{"type":"ScholarlyArticle","title":"Algorithmic Bias in Autonomous Systems","authors":[{"name":"David Danks","slug":"david-danks","given":"David","family":"Danks","affiliation":""},{"name":"Alex John London","slug":"alex-john-london","given":"Alex John","family":"London","affiliation":""}],"abstract":"Algorithms play a key role in the functioning of autonomous systems, and so concerns have periodically been raised about the possibility of algorithmic bias. However, debates in this area have been hampered by different meanings and uses of the term, \"bias.\" It is sometimes used as a purely descriptive term, sometimes as a pejorative term, and such variations can promote confusion and hamper discussions about when and how to respond to algorithmic bias. In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In some cases, there are technological or algorithmic adjustments that developers can use to compensate for problematic bias. In other cases, however, responses require adjustments by the agent, whether human or autonomous system, who uses the results of the algorithm. There is no \"one size fits all\" solution to algorithmic bias.","doi":"10.24963/ijcai.2017/654","url":"https://ijcai.org/proceedings/2017/654","pdf":"https://ijcai.org/proceedings/2017/0654.pdf","markdown":"https://ijcai.org/proceedings/2017/654.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/654","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"AI and autonomy track","track":"AI and autonomy track","number":654,"pages":{"first":4691,"last":4697},"keywords":["Social: Challenges","Philosophical: Concepts","Technical: Techniques"],"videos":[],"references":[{"position":1,"raw":"[Arkin et al., 2012] Ronald C. Arkin, Patrick Ulam, and Alan R. Wagner. Moral decision-making in autonomous systems: Enforcement, moral emotions, dignity, trust and deception. Proceedings of the IEEE, 100(3): 571-589. March 2012.","title":"Moral decision-making in autonomous systems: Enforcement, moral emotions, dignity, trust and deception","authors":"Ronald C Arkin, Patrick Ulam, Alan R Wagner","year":"2012","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Barocas and Selbst, 2016] Solon Barocas and Andrew D. Selbst. Big Data's disparate impact. California Law Review, 104: 671-732. 2016.","title":"Big Data's disparate impact","authors":"Selbst Barocas, Solon Barocas, Andrew D Selbst","year":"2016","venue":"California Law Review","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Garcia, 2016] Megan Garcia. Racist in the machine: The disturbing implications of algorithmic bias. World Policy Journal, 33(4): 111-117. Winter 2016.","title":"Racist in the machine: The disturbing implications of algorithmic bias","authors":"Megan Garcia, Garcia","year":"2016","venue":"World Policy Journal","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Geman et al., 1992] Stuart Geman, Elie Bienenstock, and René Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4: 1-58. 1992.","title":"Neural networks and the bias/variance dilemma","authors":"Stuart Geman, Elie Bienenstock, René Doursat","year":"1992","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Kirkpatrick, 2016] Keith Kirkpatrick. Battling algorithmic bias. Communications of the ACM, 59(10): 16-17. October 2016.","title":"Battling algorithmic bias","authors":"Keith Kirkpatrick","year":"2016","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Pedreschi et al., 2008] Dino Pedreschi, Salvatore Ruggieri, and Franco Turini. Discrimination-aware data mining. Proceedings of KDD 2008.","title":"Discrimination-aware data mining","authors":"Dino Pedreschi, Salvatore Ruggieri, Franco Turini","year":null,"venue":"Proceedings of KDD 2008","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[ProPublica, 2016] Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine bias. May 23, 2016. https://www.propublica.org/article/machine-bias-risk- assessments-in-criminal-sentencing","title":"Machine bias","authors":"Julia Angwin, Jeff Larson, Surya Mattu, Lauren Kirchner","year":"2016","venue":null,"doi":null,"url":"https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing","internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}