Understanding artificial intelligence ethics and safety (1906.05684v1)

Abstract: A remarkable time of human promise has been ushered in by the convergence of the ever-expanding availability of big data, the soaring speed and stretch of cloud computing platforms, and the advancement of increasingly sophisticated machine learning algorithms. Innovations in AI are already leaving a mark on government by improving the provision of essential social goods and services from healthcare, education, and transportation to food supply, energy, and environmental management. These bounties are likely just the start. The prospect that progress in AI will help government to confront some of its most urgent challenges is exciting, but legitimate worries abound. As with any new and rapidly evolving technology, a steep learning curve means that mistakes and miscalculations will be made and that both unanticipated and harmful impacts will occur. This guide, written for department and delivery leads in the UK public sector and adopted by the British Government in its publication, 'Using AI in the Public Sector,' identifies the potential harms caused by AI systems and proposes concrete, operationalisable measures to counteract them. It stresses that public sector organisations can anticipate and prevent these potential harms by stewarding a culture of responsible innovation and by putting in place governance processes that support the design and implementation of ethical, fair, and safe AI systems. It also highlights the need for algorithmically supported outcomes to be interpretable by their users and made understandable to decision subjects in clear, non-technical, and accessible ways. Finally, it builds out a vision of human-centred and context-sensitive implementation that gives a central role to communication, evidence-based reasoning, situational awareness, and moral justifiability.

A Comprehensive Guide to AI Ethics and Safety in Public Sector AI Systems

The paper "Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector" by David Leslie offers a detailed examination of ethical guidelines for designing and implementing AI systems. This guide primarily addresses government entities involved in AI projects, providing a framework to ensure that AI technologies serve the public interest in an ethical, fair, and trustworthy manner.

Key Components of the Ethical Framework

The document delineates a comprehensive approach to ethical AI through three foundational components: the SUM Values, the FAST Track Principles, and a Process-Based Governance Framework (PBG Framework). Together, these elements constitute an "ethical platform" aimed at integrating ethics into all phases of an AI system's lifecycle.

• SUM Values: A value-based framework comprising Respect, Connect, Care, and Protect. These dimensions aim to embed ethical considerations into AI projects, ensuring the respect for individual dignity, fostering inclusive connections, prioritizing wellbeing, and safeguarding social justice.
• FAST Track Principles: Comprising Fairness, Accountability, Sustainability, and Transparency, these principles provide actionable guidelines for AI system development. The framework mandates minimizing bias (Fairness), maintaining traceability and answerability (Accountability), considering long-term impacts (Sustainability), and ensuring clear, understandable system operations (Transparency).
• Process-Based Governance Framework (PBG Framework): This framework supports the operationalization of the ethical platform by creating structured governance measures throughout the design and implementation processes. It enables comprehensive oversight and auditability integral to the ethical delivery of AI technologies.

Addressing Critical Risks and Implementing Governance

The guide puts particular emphasis on addressing potential harms associated with AI systems, including biases, invasions of privacy, non-transparency, and outcomes that may undermine public trust. It elaborates on measures to mitigate these risks through effective data management, fairness-aware design, and adherence to transparency and accountability standards.

For instance, in confronting biases, the guide discusses Data Fairness, ensuring datasets are representative, relevant, and managed with integrity. Similarly, it highlights the necessity of Design Fairness, requiring conscious decisions at all project stages to avoid structural biases that could affect AI outcomes.

Moreover, the guide underscores the importance of Human-Centered Design and implementation strategies that bolster system interpretability and address end-user perspectives. The outlined strategies advocate for systems that not only adhere to technical norms but also align with social and ethical contexts, thereby promoting trust and understanding among stakeholders.

Implications and Future Prospects

The framework set forth by Leslie has considerable implications for the operationalization of ethical AI within public services. By integrating ethical scrutiny into AI systems' life cycles, the guide aims to minimize unintended consequences and foster trust in transformative technologies. The approach suggested can also inform policy-making processes by providing clear ethical benchmarks for evaluating AI deployments in public sectors.

Looking ahead, this guide informs future developments in responsible AI innovation by setting benchmarks for ethical governance practices. It anticipates ongoing discourse in AI ethics, urging practitioners to iterate upon these foundational principles to accommodate evolving technological landscapes and societal norms.

By aligning AI innovation with human-centered values, Leslie's guide establishes itself as an essential resource for public sector entities seeking to responsibly harness the potential of AI technologies. Its application promises to enhance trust and acceptance of AI systems by ensuring these technologies advance public welfare without compromising foundational ethical standards.