Deploying AI in decision-making, particularly in healthcare and criminal justice, raises significant ethical concerns. In healthcare, AI can enhance diagnostics and treatment but may also lead to decisions that lack human empathy. In criminal justice, AI could perpetuate existing biases, leading to unjust outcomes.

Ensuring accountability and transparency is crucial. This can be achieved through stringent regulatory frameworks, regular audits, and clear documentation of AI systems’ decision-making processes. Transparent algorithms and open data practices allow stakeholders to understand how decisions are made.

Addressing bias in AI models is vital to prevent harm to marginalized communities. Bias can arise from unrepresentative training data or flawed algorithmic design. Identifying bias requires diverse datasets and continuous monitoring. Techniques like fairness-aware machine learning and adversarial testing can help.

Effective mitigation strategies include:
1. Diverse and inclusive data collection to ensure representation.
2. Bias auditing tools to detect and measure bias.
3. Algorithmic transparency to allow for external review.
4. Involving ethicists and community representatives in the development process.

Regularly updating models and incorporating feedback loops can also help in adapting to changing societal norms and reducing bias over time.