Governing artificial intelligence: ethical, legal and technical opportunities and challenges

By Corinne Cath. 2018

561 citations recorded by the source corpus.

This paper is the introduction to the special issue entitled: ‘Governing artificial intelligence: ethical, legal and technical opportunities and challenges'. Artificial intelligence (AI) increasingly permeates every aspect of our society, from the critical, like urban infrastructure, law enforcement, banking, healthcare and humanitarian aid, to the mundane like dating. AI, including embodied AI in robotics and techniques like machine learning, can improve economic, social welfare and the exercise of human rights. Owing to the proliferation of AI in high-risk areas, the pressure is mounting to design and govern AI to be accountable, fair and transparent. How can this be achieved and through which frameworks? This is one of the central questions addressed in this special issue, in which eight authors present in-depth analyses of the ethical, legal-regulatory and technical challenges posed by developing governance regimes for AI systems. It also gives a brief overview of recent developments in AI governance, how much of the agenda for defining AI regulation, ethical frameworks and technical approaches is set, as well as providing some concrete suggestions to further the debate on AI governance. This article is part of the theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Safety Research

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Citations in the corpus

Understanding artificial intelligence ethics and safety (2019, methodology): “The cathedral of computation. The Atlantic, 15. Retrieved from https://www.theatlantic.com/technology/archive/2015/01/the-cathedral-of-computation/384300/ Bolin, G., & Andersson Schwarz, J. (2015). Heuristics of the algorithm: Big Data, user interpretation and institutional translation. Big Data & S”
A governance model for the application of AI in health care (2019, background): “As discussed, inappropriate and poorly representative training datasets for AI models can lead to biases, inaccurate predictions, medical errors, and even large scale discrimination.(3,5,8) Therefore, we recommend a data governance panel constituted by the AI developers that includes patient and tar”
Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector (2023, unknown): “To overcome these hindrances, possible solutions have been suggested to deal with the issues of fairness, accountability, and transparency through the implementation of ethical governance, model explain ability, model inter-pretability, and ethical auditing [29].”
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “Cath [22] Perceived liability issues The extent to which an individual is concerned about the liability and responsibility of using AI clinical tools.”
From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “2017), so too does the need to be critical of pro-ethical (Floridi, 2016) AI governance (Cath, 2018) and design solutions.”
Artificial intelligence in business: State of the art and future research agenda (2020, background): “Social scientists have been discussing ethical and legal implications of AI (Cath, 2018), computer scientists have developed advanced deep learning algorithms (LeCun, Bengio, & Hinton, 2015), while researchers in business management have studied the impacts of AI on customers, firms, and stakeholder”
From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices (2019, background): “However, as the complexity of algorithmic systems and our reliance on them increases (Cath et al. 2017), so too does the need to be critical (Floridi 2016a) AI governance (Cath 2018) and design solutions.”