This POSTnote provides an overview of machine learning (ML) and its role in decision-making, highlighting the challenges of understanding how complex ML systems reach their outputs. It discusses technical approaches to making ML more interpretable and introduces tools for enhancing accountability, such as algorithm audits and impact assessments. The note emphasizes the importance of interpretability in ensuring transparency and accountability, particularly in applications with significant impacts on individuals. It also addresses the issue of algorithmic bias and the need for ethical ML practices. The UK government has taken steps to promote ethical ML through initiatives like the Data Ethics Framework and the Centre for Data Ethics and Innovation. The note outlines various legal frameworks relevant to ML decision-making, including data protection laws and human rights considerations. It explores the benefits and challenges of interpretability, including improved performance, user trust, and regulatory compliance. Additionally, it discusses wider accountability mechanisms such as open and documented processes, fact sheets, algorithmic impact assessments, and certification.This POSTnote provides an overview of machine learning (ML) and its role in decision-making, highlighting the challenges of understanding how complex ML systems reach their outputs. It discusses technical approaches to making ML more interpretable and introduces tools for enhancing accountability, such as algorithm audits and impact assessments. The note emphasizes the importance of interpretability in ensuring transparency and accountability, particularly in applications with significant impacts on individuals. It also addresses the issue of algorithmic bias and the need for ethical ML practices. The UK government has taken steps to promote ethical ML through initiatives like the Data Ethics Framework and the Centre for Data Ethics and Innovation. The note outlines various legal frameworks relevant to ML decision-making, including data protection laws and human rights considerations. It explores the benefits and challenges of interpretability, including improved performance, user trust, and regulatory compliance. Additionally, it discusses wider accountability mechanisms such as open and documented processes, fact sheets, algorithmic impact assessments, and certification.