Reducing Power Inequality in the Age of AI and the Internet: A Practical Summary of Experience Using Toolkits

Sixuan Liu

doi:10.54097/kst07591

Authors

Sixuan Liu

DOI:

https://doi.org/10.54097/kst07591

Keywords:

Toolkit, Technology Ethics, Machine Learning, and AI.

Abstract

This article discusses the potential power inequalities caused by algorithmic bias in the age of artificial intelligence, aiming to guide technology companies in identifying biases throughout the AI lifecycle and taking effective measures to reduce discrimination arising from technology and algorithms. It emphasizes the importance of algorithmic process transparency, advocates for increased diversity within AI development teams, and promotes designing frameworks based on the dataset to ensure that harmful biases do not permeate AI system functionality. These measures help improve the ethical application of AI technology and enhance the company's credibility and moral standing. Furthermore, the toolkit will assist stakeholders in the technology field in making informed decisions and guide decision-makers in building a sustainable AI ecosystem from an ethical standpoint. In this ecosystem, the advancements in AI enable all segments of society to benefit without discrimination.

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References

[1] Accenture. (n.d.). AI Fairness Tool. Responsible AI Governance Consulting & Solutions | Accenture.Retrieved form https://www.accenture.com/us-en/services/data-ai/responsible-ai.

[2] AI Now Institute. (n.d.). Research and Initiatives. Home - AI Now Institute. Retrieved from https://ainowinstitute.org.

[3] Bateni, A., Chan, M. C., & Eitel-Porter, R. (2022). AI fairness: from principles to practice. Accenture.

[4] Cai, F., Zhang, J., & Zhang, L. (2024). The impact of artificial intelligence replacing humans in making human resource management decisions on fairness: A case of resume screening. Sustainability, 16, 3840. https://doi.org/10.3390/su16093840.

[5] Google AI. (n.d.). People + AI Research (PAIR). Retrieved from https://pair.withgoogle.com.

[6] IBM Corporation. (n.d.). AI Fairness 360. Retrieved from https://aif360.res.ibm.com.

[7] Islam, R., Keya, K.N., Pan, S., Sarwate, A.D., & Foulds, J.R. (2023). Differential fairness: An intersectional framework for fair AI. Entropy, 25, 660. https://doi.org/10.3390/e25040660.

[8] Liu, M., Ning, Y., Teixayavong, S., Mertens, M., Xu, J., Ting, D. S. W., Cheng, L. T. E., Ong, J. C. L., Teo, Z. L., Tan, T. F., RaviChandran, N., Wang, F., Celi, L. A., Ong, M. E. H., & Liu, N. (2023). A translational perspective towards clinical AI fairness. npj Digital Medicine, 6:172. https://doi.org/10.1038/s41746-023-00918-4.

[9] Microsoft. (n.d.). Responsible AI Standard. Empowering responsible AI practices | Microsoft AI. Retrieved from https://www.microsoft.com/en-us/ai/responsible-ai.

[10] MIT Media Lab. (n.d.). Moral Machine. Retrieved from https://www.moralmachine.net.

[11] Robert, L. P., Pierce, C., Morris, L., Kim, S., & Alahmad, R. (2020). Designing fair AI for managing employees in organizations: A review, critique, and design agenda. Human–Computer Interaction. Accepted to the Special Issue on Unifying Human Computer Interaction and Artificial Intelligence.

[12] The Ada Lovelace Institute. (n.d.). About Us. Retrieved from https://www.adalovelaceinstitute.org/policy-briefing/ai-safety/.

[13] Unilever Global. (n.d.). Enhancing Livelihoods, Advancing Human Rights. Retrieved from https://www.unilever.com/sustainability/.

[14] Xivuri, K., & Twinomurinzi, H. (2023). How AI developers can assure algorithmic fairness. Discover Artificial Intelligence, 3:27. https://doi.org/10.1007/s44163-023-00074-4.