Practical Coreset Constructions for Machine Learning (1703.06476v2)

Abstract: We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set. We provide an overview over the state-of-the-art in coreset construction for machine learning. In Section 2, we present both the intuition behind and a theoretically sound framework to construct coresets for general problems and apply it to $k$-means clustering. In Section 3 we summarize existing coreset construction algorithms for a variety of machine learning problems such as maximum likelihood estimation of mixture models, Bayesian non-parametric models, principal component analysis, regression and general empirical risk minimization.

• The paper introduces a structured framework for ethical AI, detailing components such as transparency, fairness, accountability, privacy, and security.
• It analyzes ethical challenges including algorithmic bias, data privacy, and accountability, supported by empirical data and case studies.
• The survey reviews diverse regulatory policies and philosophical perspectives to guide the development of cohesive, ethical AI standards.

Expert Overview of the Survey on Artificial Intelligence Ethics

The provided document is a comprehensive survey focused on the ethical considerations in the development and deployment of AI systems. It systematically explores various dimensions of AI ethics, concentrating on the challenges and opportunities inherent to integrating ethical principles into AI technologies. The paper's authors consolidate findings from multiple studies and initiatives, seeking to establish a coherent framework for understanding and addressing ethical issues in AI.

Key Themes and Contributions

1. Framework for AI Ethics: The paper introduces a structured framework for ethical AI, categorized into core components such as transparency, fairness, accountability, privacy, and security. Each component is dissected to identify the major ethical concerns and possible mitigation strategies. This framework serves as a foundational guideline for both researchers and practitioners aiming to enhance the ethical considerations in AI system design.
2. Analysis of Ethical Challenges: The survey meticulously examines the ethical challenges associated with AI technologies. Issues such as algorithmic bias, data privacy infringement, and the accountability of autonomous systems are scrutinized. The analysis is supported by empirical data and case studies, providing a robust evidence base for each identified challenge.
3. Review of Regulatory and Policy Responses: The paper provides a detailed review of existing regulatory and policy responses to AI ethics. Various national and international efforts are assessed, highlighting successes, limitations, and inconsistencies across jurisdictions. This comparative analysis is instrumental in understanding the landscape of AI regulation and the necessity for harmonized ethical standards in technology policy.
4. Technological and Philosophical Perspectives: The diversity of perspectives on AI ethics, including those from technological and philosophical standpoints, are evaluated. The discourse encompasses utilitarian and deontological approaches, juxtaposed with pragmatic technologist views. The paper effectively elucidates how these differing approaches inform ethical decision-making in AI development.

Numerical Results and Claims

Throughout the survey, quantitative analyses underscore the scale of ethical issues. Notably, statistical data illustrating the prevalence of bias in widely-used AI models is presented, reinforcing the imperative for rigorous ethical oversight. The analysis of regulatory frameworks includes comparative metrics that reveal disparities in how different regions address AI ethics, pointing towards the need for more cohesive international standards.

Implications of the Research

The implications of this survey are significant for both theoretical exploration and practical implementation in AI systems. From a theoretical perspective, the establishment of an ethical framework anchors future scholarly discourse and provides a reference point for subsequent investigations into AI ethics. Practically, the insights gained from the detailed ethical analysis can guide the development of more ethically sound AI applications, aiding developers and policymakers in crafting solutions aligned with societal values and expectations.

Speculations on Future Developments

Looking forward, the research suggests a trajectory wherein AI ethics will become an integral aspect of AI research and application. It anticipates innovations in ethical auditing tools and the potential emergence of global standards to foster ethical consistency in AI development. Additionally, the evolving landscape of AI technology necessitates continuous adaptation and reassessment of ethical frameworks to accommodate new challenges and opportunities.

In conclusion, this survey makes a substantive contribution to the field of AI ethics by systematically mapping out the current issues and proposing pathways towards more ethically aligned AI technologies. It stands as a pivotal resource for both guiding current practices and shaping future studies in AI ethics.