Tam Nga Man (4176845) Gender and racial biases in AI Gender Shades project by MIT 9 ● 3.6% of faces misgendered by Microsoft were those of darker subjects. ● 95.9% of the faces misgendered by Face++ were those of female subjects. Dangers of a bias AI ❖ Widespread, mysterious and destructive algorithms (Weapons of Math Destruction, O’neil, 2016) ❖ it’s not just about how these algorithms are harming us — it’s about how these algorithms harm us in ways we do not know, do not know we do not know, and do not currently have means to prove and challenge. ➢ someone wrongfully accused of a crime based on misidentification of the perpetrator from security video footage analysis by AI ➢ AI screening out female candidates for an engineering position bc the company used to only hire male for the same position ➢ Crime-fighting AI determining a black person to be more likely a criminal reoffender than a white person ➢ Recommendation algorithm not showing certain job positions to female users ➢ … etc. Big data =/= Good data (Kazimzade, 2019) ● ● ● ● hen and how was the data collected? W Collected by whom? Collected for what purposes? Does it contain any historical or ideological mappings? Tam Nga Man (4176845) How about we only collect relevant data? (Dobrin, 2020) ● e .g. How about we don’t use irrelevant data (like gender) as a factor to decide how many credits you can have with your new credit card? ○ The Apple Card example ■ given a credit limit that was 10 times lower than her husband. They share all the same assets and they share all the same accounts ■ “While they explicitly removed gender (as a factor), there were other factors associated with gender that the AI algorithm identified in order to classify individuals based on a perceived risk.” ○ “The bank deciding if mortgages should be given” example ■ They ensured that the algorithm didn’t have any gender, racial, religious or ethnic bias, but… ● Seemingly unrelated pieces of data might be closely related ○ “Because the model was tainted by a historical bias in the data, they were making a bunch of bad decisionsreally fast.” Keeping algorithmic bias out of AI (Sharma, 2019) . Be aware of our own biases and the bias in machines around us 1 2. Make sure that full-spectrum teams with diverse individuals that are developing systems 3. Give the AI diverse data to learn from Inclusive coding (Buolamwini, 2017) W ● ho codes? ● How do we code? ○ Algorithmic auditing to uncover problematic patterns ■ a process through which an automated decision system (ADS) or algorithmic product, tool, or platform (also referred to here under the umbrella term ‘AI system’) is evaluated. ● Why do we code? Algorithmic Justice League Policy recommendations 1. Require the owners and operators of ai systems to engage in independent algorithmic audits against clearly defined standards 2. Notify individuals when they are subject to algorithmic decision-making systems 3. Mandate disclosure of key components of audit findings for peer review 4. Consider real-world harm in the audit process, including through standardized harm incident reporting and response mechanisms 5. Directly involve the stakeholders most likely to be harmed by ai systems in the algorithmic audit process 6. Formalize evaluation and, potentially, accreditation of algorithmic auditors Tam Nga Man (4176845) References “ Results,” Gender Shades, MIT Media Lab, MIT Civic Media, accessed 17 Apr 2024, http://gendershades.org/overview.html. Cathy O’Neil. Weapons of Math Destruction. United States:Crown Books. 2016. unay Kazimzade, “Racial and Gender Biases in AI,” filmed at TEDxHUBerlin, uploaded 14 G Mar 2019,https://www.youtube.com/watch?v=cg_5t_bV4XE&ab_channel=TEDxTalks. eth Dobrin, “Tackling AI Bias is a human problem,” filmed at TEDxUniversityatBuffalo, S uploaded 2 Sept 2020, https://www.youtube.com/watch?v=rWU83MK7t9c&ab_channel=TEDxTalks. riti Sharma, “How to keep human bias out of AI,” uploaded 13 Apr 2019, K https://www.youtube.com/watch?v=BRRNeBKwvNM&ab_channel=TED. oy Buolamwini, “How I'm fighting bias in algorithms,” uploaded 30 Mar 2017, J https://www.youtube.com/watch?v=UG_X_7g63rY&ab_channel=TED. asha Costanza-Chock, Emmaharvey, Inioluwa Deborah Raji, Martha Czernuszenko, Joy S Buolamwini. “Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem.” Algorithmic Justice League. 4 Oct 2023. https://www.ajl.org/auditors.
