Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review

• URL: http://arxiv.org/abs/2206.09514v3
• Date: Tue, 20 Feb 2024 00:39:52 GMT
• Title: Ethics in AI through the Practitioner's View: A Grounded Theory Literature Review
• Authors: Aastha Pant, Rashina Hoda, Chakkrit Tantithamthavorn, Burak Turhan
• Abstract summary: In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners' views on ethics in AI. We present a taxonomy of ethics in AI from practitioners' viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics.
• Score: 12.941478155592502
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems- the AI practitioners? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI practitioners' views on ethics in AI and analysed them to derive five categories: practitioner awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from practitioners' viewpoints to assist AI practitioners in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI practitioners when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for practitioners, managers, and organisations to help in their efforts to better consider and implement ethics in AI.

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