Algorithmic Impact Assessment for an Ethical Use of AI in SMEs

With the ever increasing reliance on artificial systems to automate the decision process of smart systems that have the potential to affect our daily lives, the question of how to attribute liability is becoming more and more relevant, especially when human control over technical systems is increasingly reduced. This study aims to provide an overview on algorithmic impact assessment for socio-technical systems, with a focus on the challenges for its adoption by small and medium enterprises.

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Author and article information

Contributors

Shefeh Prisilia Mbuy

Marco Ortolani

Conference

Publication date: July 2022

Publication date (Print): July 2022

Pages: 1-8

Affiliations

[0001]School of Computing and Mathematics

Keele University, UK

Article

DOI: 10.14236/ewic/HCI2022.34

SO-VID: a93edf70-1e82-4663-85d1-0b21ef0a770d

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 35th International BCS Human-Computer Interaction Conference

Conference acronym: HCI2022

Conference number: 35

Conference location: Keele, Staffordshire

Conference date: July 11th to 13th, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Towards a Human-Centred Digital Society

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.34

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Ada Lovelace Institute (2021). Technical methods for the regulatory inspection of algorithmic systems in social media platforms. Technical report, Ada Lovelace Institute.
Ada Lovelace Institute (2022). Algorithmic impact assessment: a case study in healthcare. Technical report, Ada Lovelace Institute.
Adamopoulou, E. and L. Moussiades (2020). Chatbots: History, technology, and applications. Machine Learning with Applications 2, 100006.
Al-Zubaide, H. and A. A. Issa (2011). Ontbot: Ontology based chatbot. In International Symposium on Innovations in Information and Communications Technology, pp. 7–12. IEEE.
Angwin, J., J. Larson, S. Mattu, and L. Kirchner (2016). Machine bias. In Ethics of Data and Analytics, pp. 254–264. Auerbach Publications.
Beutel, A., J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi (2019). Putting fairness principles into practice: Challenges, metrics, and improvements. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 453–459.
Buolamwini, J. and T. Gebru (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency, pp. 77–91. PMLR.
Ciechanowski, L., A. Przegalinska, M. Magnuski, and P. Gloor (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems 92, 539–548.
Cobbe, J., M. S. A. Lee, and J. Singh (2021). Reviewable automated decision-making: A framework for accountable algorithmic systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 598–609.
Floridi, L. (2019). Translating principles into practices of digital ethics: Five risks of being unethical. Philosophy & technology 32(2), 185–193.
Guzman, A., S. Ishida, E. Choi, and A. Aoyama (2016). Artificial intelligence improving safety and risk analysis: A comparative analysis for critical infrastructure. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 471–475. IEEE.
Hagendorff, T. (2020). The ethics of ai ethics: An evaluation of guidelines. Minds and machines 30(1), 99–120.
Jobin, A., M. Ienca, and E. Vayena (2019). The global landscape of ai ethics guidelines. Nature Machine Intelligence 1(9), 389–399.
Kaminski, M. E. and G. Malgieri (2020). Multi-layered explanations from algorithmic impact assessments in the gdpr. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 68–79.
Khanna, A., B. Pandey, K. Vashishta, K. Kalia, B. Pradeepkumar, and T. Das (2015). A study of today’s ai through chatbots and rediscovery of machine intelligence. International Journal of u-and e-Service, Science and Technology 8(7), 277–284.
Metcalf, J., E. Moss, E. A. Watkins, R. Singh, and M. C. Elish (2021). Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 735–746.
Morley, J., L. Floridi, L. Kinsey, and A. Elhalal (2019). From what to how: An initial review of publicly available ai ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics 26(4), 2141–2168.
Raji, I. D., A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, and P. Barnes (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 33–44.
Selbst, A. D. (2021). An institutional view of algorithmic impact assessments. Harvard Journal of Law & Technology 117(35), 75.
Shawar, B. A. and E. Atwell (2007). Chatbots: are they really useful? In Ldv forum, Volume 22, pp. 29–49.
Smuha, N. A., E. Ahmed-Rengers, A. Harkens,W. Li, J. MacLaren, R. Piselli, and K. Yeung (2021). How the eu can achieve legally trustworthy ai: A response to the european commission’s proposal for an artificial intelligence act. Available at SSRN 3899991.
Suresh, H. and J. Guttag (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and Access in Algorithms, Mechanisms, and Optimization, pp. 1–9.
The Government of Canada (2018). Algorithmic impact assessment tool.
Thomas, R. L. and D. Uminsky (2022). Reliance on metrics is a fundamental challenge for ai. Patterns 3(5), 100476.
Van Esch, P., J. S. Black, and J. Ferolie (2019). Marketing ai recruitment: The next phase in job application and selection. Computers in Human Behavior 90, 215–222.
Washington, A. L. (2018). How to argue with an algorithm: Lessons from the compas-propublica debate. Colorado Technology Law Journal 17, 137.
Watkins, E. A., E. Moss, J. Metcalf, R. Singh, and M. C. Elish (2021). Governing algorithmic systems with impact assessments: Six observations. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 1010–1022. Association for Computing Machinery.
Whittaker, M., K. Crawford, R. Dobbe, G. Fried, E. Kaziunas, V. Mathur, S. M. West, R. Richardson, J. Schultz, and O. Schwartz (2018). Ai now report 2018. Technical report, AI Now Institute at New York University.
Wieringa, M. (2020). What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pp. 1–18.
Yeung, K. (2020). Recommendation of the council on artificial intelligence (oecd). International legal materials 59(1), 27–34.

Comments

Comment on this article

[1] Ada Lovelace Institute (2021). Technical methods for the regulatory inspection of algorithmic systems in social media platforms. Technical report, Ada Lovelace Institute.

[2] Ada Lovelace Institute (2022). Algorithmic impact assessment: a case study in healthcare. Technical report, Ada Lovelace Institute.

[3] Adamopoulou, E. and L. Moussiades (2020). Chatbots: History, technology, and applications. Machine Learning with Applications 2, 100006.

[4] Al-Zubaide, H. and A. A. Issa (2011). Ontbot: Ontology based chatbot. In International Symposium on Innovations in Information and Communications Technology, pp. 7–12. IEEE.

[5] Angwin, J., J. Larson, S. Mattu, and L. Kirchner (2016). Machine bias. In Ethics of Data and Analytics, pp. 254–264. Auerbach Publications.

[6] Beutel, A., J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi (2019). Putting fairness principles into practice: Challenges, metrics, and improvements. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pp. 453–459.

[7] Buolamwini, J. and T. Gebru (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency, pp. 77–91. PMLR.

[8] Ciechanowski, L., A. Przegalinska, M. Magnuski, and P. Gloor (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. Future Generation Computer Systems 92, 539–548.

[9] Cobbe, J., M. S. A. Lee, and J. Singh (2021). Reviewable automated decision-making: A framework for accountable algorithmic systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 598–609.

[10] Floridi, L. (2019). Translating principles into practices of digital ethics: Five risks of being unethical. Philosophy & technology 32(2), 185–193.

[11] Guzman, A., S. Ishida, E. Choi, and A. Aoyama (2016). Artificial intelligence improving safety and risk analysis: A comparative analysis for critical infrastructure. In 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 471–475. IEEE.

[12] Hagendorff, T. (2020). The ethics of ai ethics: An evaluation of guidelines. Minds and machines 30(1), 99–120.

[13] Jobin, A., M. Ienca, and E. Vayena (2019). The global landscape of ai ethics guidelines. Nature Machine Intelligence 1(9), 389–399.

[14] Kaminski, M. E. and G. Malgieri (2020). Multi-layered explanations from algorithmic impact assessments in the gdpr. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 68–79.

[15] Khanna, A., B. Pandey, K. Vashishta, K. Kalia, B. Pradeepkumar, and T. Das (2015). A study of today’s ai through chatbots and rediscovery of machine intelligence. International Journal of u-and e-Service, Science and Technology 8(7), 277–284.

[16] Metcalf, J., E. Moss, E. A. Watkins, R. Singh, and M. C. Elish (2021). Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 735–746.

[17] Morley, J., L. Floridi, L. Kinsey, and A. Elhalal (2019). From what to how: An initial review of publicly available ai ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics 26(4), 2141–2168.

[18] Raji, I. D., A. Smart, R. N. White, M. Mitchell, T. Gebru, B. Hutchinson, J. Smith-Loud, D. Theron, and P. Barnes (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 33–44.

[19] Selbst, A. D. (2021). An institutional view of algorithmic impact assessments. Harvard Journal of Law & Technology 117(35), 75.

[20] Shawar, B. A. and E. Atwell (2007). Chatbots: are they really useful? In Ldv forum, Volume 22, pp. 29–49.

[21] Smuha, N. A., E. Ahmed-Rengers, A. Harkens,W. Li, J. MacLaren, R. Piselli, and K. Yeung (2021). How the eu can achieve legally trustworthy ai: A response to the european commission’s proposal for an artificial intelligence act. Available at SSRN 3899991.

[22] Suresh, H. and J. Guttag (2021). A framework for understanding sources of harm throughout the machine learning life cycle. In Equity and Access in Algorithms, Mechanisms, and Optimization, pp. 1–9.

[23] The Government of Canada (2018). Algorithmic impact assessment tool.

[24] Thomas, R. L. and D. Uminsky (2022). Reliance on metrics is a fundamental challenge for ai. Patterns 3(5), 100476.

[25] Van Esch, P., J. S. Black, and J. Ferolie (2019). Marketing ai recruitment: The next phase in job application and selection. Computers in Human Behavior 90, 215–222.

[26] Washington, A. L. (2018). How to argue with an algorithm: Lessons from the compas-propublica debate. Colorado Technology Law Journal 17, 137.

[27] Watkins, E. A., E. Moss, J. Metcalf, R. Singh, and M. C. Elish (2021). Governing algorithmic systems with impact assessments: Six observations. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, pp. 1010–1022. Association for Computing Machinery.

[28] Whittaker, M., K. Crawford, R. Dobbe, G. Fried, E. Kaziunas, V. Mathur, S. M. West, R. Richardson, J. Schultz, and O. Schwartz (2018). Ai now report 2018. Technical report, AI Now Institute at New York University.

[29] Wieringa, M. (2020). What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pp. 1–18.

[30] Yeung, K. (2020). Recommendation of the council on artificial intelligence (oecd). International legal materials 59(1), 27–34.

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