Envisioning Equitable Speech Technologies for Black Older Adults

There is increasing concern that how researchers currently define and measure fairness is inadequate. Recent calls push to move beyond traditional concepts of fairness and consider related constructs through qualitative and community-based approaches, particularly for underrepresented communities most at-risk for AI harm. One in context, previous research has identified that voice technologies are unfair due to racial and age disparities. This paper uses voice technologies as a case study to unpack how Black older adults value and envision fair and equitable AI systems. We conducted design workshops and interviews with 16 Black older adults, exploring how participants envisioned voice technologies that better understand cultural context and mitigate cultural dissonance. Our findings identify tensions between what it means to have fair, inclusive, and representative voice technologies. This research raises questions about how and whether researchers can model cultural representation with large language models.

[1]  Cosmin Munteanu,et al.  Does Alexa Live Up to the Hype? Contrasting Expectations from Mass Media Narratives and Older Adults' Hands-on Experiences of Voice Interfaces , 2022, CUI.

[2]  Casey S. Pierce,et al.  An Empirical Study of Older Adult’s Voice Assistant Use for Health Information Seeking , 2022, ACM Trans. Interact. Intell. Syst..

[3]  Sachin Pathiyan Cherumanal.  Fairness-Aware Question Answering for Intelligent Assistants , 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

[4]  Youjin Kong.  Are “Intersectionally Fair” AI Algorithms Really Fair to Women of Color? A Philosophical Analysis , 2022, FAccT.

[5]  P. Lohia,et al.  Beyond Fairness: Reparative Algorithms to Address Historical Injustices of Housing Discrimination in the US , 2022, FAccT.

[6]  Marilyn Zhang.  Affirmative Algorithms: Relational Equality as Algorithmic Fairness , 2022, FAccT.

[7]  G. Gu,et al.  BiasHacker: Voice Command Disruption by Exploiting Speaker Biases in Automatic Speech Recognition , 2022, WISEC.

[8]  Christopher L. Dancy,et al.  The Forgotten Margins of AI Ethics , 2022, Conference on Fairness, Accountability and Transparency.

[9]  Robin N. Brewer,et al.  “If Alexa knew the state I was in, it would cry”: Older Adults’ Perspectives of Voice Assistants for Health , 2022, CHI Extended Abstracts.

[10]  McKane Andrus,et al.  Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness , 2022, FAccT.

[11]  Seth Polsley,et al.  Inequity in Popular Speech Recognition Systems for Accented English Speech , 2022, IUI Companion.

[12]  A. Ding,et al.  Bias in Automated Speaker Recognition , 2022, FAccT.

[13]  Christina N. Harrington,et al.  “It’s Kind of Like Code-Switching”: Black Older Adults’ Experiences with a Voice Assistant for Health Information Seeking , 2021, CHI.

[14]  Jenny Waycott,et al.  Swipe a Screen or Say the Word: Older Adults’ Preferences for Information-seeking with Touchscreen and Voice-User Interfaces , 2021, OZCHI.

[15]  Courtney Heldreth,et al.  “I don’t Think These Devices are Very Culturally Sensitive.”—Impact of Automated Speech Recognition Errors on African Americans , 2021, Frontiers in Artificial Intelligence.

[16]  Rediet Abebe,et al.  Fairness, Equality, and Power in Algorithmic Decision-Making , 2021, FAccT.

[17]  Michelle Cohn,et al.  Age- and Gender-Related Differences in Speech Alignment Toward Humans and Voice-AI , 2021, Frontiers in Communication.

[18]  W. Rogers,et al.  Digital Home Assistants and Aging: Initial Perspectives from Novice Older Adult Users , 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[19]  Gianni Fenu,et al.  Improving Fairness in Speaker Recognition , 2020, ESSE.

[20]  Elena Spitzer,et al.  What We Can't Measure, We Can't Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness , 2020, FAccT.

[21]  Joshua L. Martin,et al.  Understanding Racial Disparities in Automatic Speech Recognition: The Case of Habitual "be" , 2020, INTERSPEECH.

[22]  Ana-Andreea Stoica,et al.  Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness , 2020, FAccT.

[23]  Amanda Lazar,et al.  Use of Intelligent Voice Assistants by Older Adults with Low Technology Use , 2020, ACM Trans. Comput. Hum. Interact..

[24]  Aqueasha Martin-Hammond,et al.  "Alexa is a Toy": Exploring Older Adults' Reasons for Using, Limiting, and Abandoning Echo , 2020, CHI.

[25]  Dan Jurafsky,et al.  Racial disparities in automated speech recognition , 2020, Proceedings of the National Academy of Sciences.

[26]  Karthik Dinakar,et al.  Studying up: reorienting the study of algorithmic fairness around issues of power , 2020, FAT*.

[27]  Sean A. McGlynn,et al.  Perceptions of Digital Assistants From Early Adopters Aged 55+ , 2019, Ergonomics in Design: The Quarterly of Human Factors Applications.

[28]  Reuben Binns,et al.  On the apparent conflict between individual and group fairness , 2019, FAT*.

[29]  Francis M. Tyers,et al.  Common Voice: A Massively-Multilingual Speech Corpus , 2019, LREC.

[30]  Hanna M. Wallach,et al.  Measurement and Fairness , 2019, FAccT.

[31]  Emily Denton,et al.  Towards a critical race methodology in algorithmic fairness , 2019, FAT*.

[32]  Daniel Gruen,et al.  Considerations for AI fairness for people with disabilities , 2019, SIGAI.

[33]  M. Mills,et al.  Disability, Bias, and AI , 2019 .

[34]  Benjamin R. Cowan,et al.  Voice assistants and older people: some open issues , 2019, CUI.

[35]  Krzysztof Marasek,et al.  Older Adults and Voice Interaction: A Pilot Study with Google Home , 2019, CHI Extended Abstracts.

[36]  Danah Boyd,et al.  Fairness and Abstraction in Sociotechnical Systems , 2019, FAT.

[37]  Reuben Binns,et al.  Fairness in Machine Learning: Lessons from Political Philosophy , 2017, FAT.

[38]  Rachael Tatman,et al.  Effects of Talker Dialect, Gender & Race on Accuracy of Bing Speech and YouTube Automatic Captions , 2017, INTERSPEECH.

[39]  Anne Marie Piper,et al.  Exploring Traditional Phones as an E-Mail Interface for Older Adults , 2016, ACM Trans. Access. Comput..

[40]  Carlos Eduardo Scheidegger,et al.  Certifying and Removing Disparate Impact , 2014, KDD.

[41]  D. Dunlop,et al.  Racial/ethnic differences in the development of disability among older adults. , 2007, American journal of public health.

[42]  Mark Hasegawa-Johnson,et al.  Counterfactually Fair Automatic Speech Recognition , 2021, IEEE/ACM Transactions on Audio, Speech, and Language Processing.