AI algorithms at work: How to use AI to help overcome historical biases
AI algorithms are becoming the norm without the public’s trust
Only 35% of consumers are comfortable with businesses using AI algorithms to interact with them - and only 25% said they would trust a decision made by an AI algorithm over a person regarding their qualification for a bank loan.1
Though the general public remains apprehensive, AI algorithms will inevitably become the status quo for businesses: 25% of companies already have selective processes fully enabled by AI algorithms.2 And for good reason: they’re favored across industries thanks to their efficient processing speed, consistent performance, and capacity to reduce the cost of human labor.
References
- Cannon, J. (2019) “Report shows consumers don’t trust artificial intelligence.” Fintech News,https://www.fintechnews.org/report-shows-consumers-dont-trust-artificial-intelligence/; “What Consumers Really Think About AI: A Global Study: Insights into the minds of consumers to help businesses reshape their customer engagement strategies,” Pega PowerPoint.
- Best, B., Rao A., (2022). “Understanding algorithmic bias and how to build trust in AI,” PWC https://www.pwc.com/us/en/tech-effect/ai-analytics/algorithmic-bias-and-trust-in-ai.html
- Angwin, J., Larson, J., Mattu S., Kircnur, L. (2016) Machine Bias: There’s software used across the country to predict future criminals. And it’s biased against blacks, ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
- Polli, F (2019) “Using AI to Eliminate Bias from Hiring,” Harvard Business Review, https://hbr.org/2019/10/using-ai-to-eliminate-bias-from-hiring
- Dietvorst, B., Simmons, J. P., & Massey, C. (2015). Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err. Journal of Experimental Psychology: General, 144 (1), 114-126.
- Dastin, J. (2018) Amazon Scraps Secret AI Recruiting Tool that Showed Bias Against Women, Reuters. https://www.diverseeducation.com/students/article/15114427/ai-in-admissions-can-reduce-or-reinforce-biases#:~:text=Admissions%20offices%20have%20been%20rolling,these%20emerging%20tools%20are%20used.
- Notre Dame Technology Ethics Center (2022), “New Anthology by ND TEC Director Kirsten Martin Explores ‘Ethics of Data and Analytics’” University of Notre Dame, https://techethics.nd.edu/news-and-events/news/new-anthology-by-nd-tec-director-kirsten-martin-explores-ethics-of-data-and-analytics/
- J González., C. Cortina., Rodríguez, J (2019). The Role of Gender Stereotypes in Hiring: A Field Experiment, European Sociological Review, Volume 35, Issue 2, 187–204, https://doi.org/10.1093/esr/jcy055
- Polli, F (2019) “Using AI to Eliminate Bias from Hiring,” Harvard Business Review, https://hbr.org/2019/10/using-ai-to-eliminate-bias-from-hiring
- Chiappa, S. (2019). Path-Specific Counterfactual Fairness. Proceedings of the AAAI Conference on Artificial Intelligence, 7801-7808.
- McCracken, M (2020). Breaking Down Bias in Admissions, Kira Talent, https://blog.kiratalent.com/nine-forms-of-bias-in-admissions/
About the Authors
Ariel LaFayette
Ariel is an incoming Philosophy PhD student at the University of Toronto (UofT) and specializes in hermeneutics, phenomenology, the philosophy of religion, and the history of psychology. More broadly, she is interested in how the questions posed by seminal scholars (e.g., Augustine, Kierkegaard, Gadamer) continue to influence our investigations into self-knowledge, the limitations of reason, and personal fulfillment.
Turney McKee
Turney McKee is a Director at The Decision Lab. He holds a Masters of Science in Cellular Biology and Bachelors of Science in Pharmacology, both from McGill University. He is interested in international healthcare systems and public policy. Before joining The Decision Lab, Turney worked as a competitive and business intelligence analyst in the healthcare and technology sectors.
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