Fairness and Biased Mitigation – Devs should work on techniques to detect and minimize bias, such as: re-sampling data, adversarial debiasing, & any fairness constraints in model training. AI systems should be inclusive and not propagate biases present in training data or Devs assumptions
Explainability & Transparency – Algorithm should be understandable to Developers (Devs), stakeholders, and end-users. Devs aim to build models that allow users to trace & explain decisions, especially for critical areas such as finance & healthcare.
Security & Privacy – User data protection is extremely important. Algorithms should use secure data practices such as encryption & anonymization. Devs must integrate differential privacy or federated learning to enhance data privacy.
Audit Logs & Accountability – Devs and organizations must take responsibility for any outcome of AI algorithms, i.e. good or bad. Deploying tools such as audit logs and ethical AI reviews will ensure compliance and accountability.
Human-Centric Design – Beware that AI algorithms are designed to augment human capabilities, and not replace or harm them. Devs should emphasize usability, accessibility, and adaptability to different user needs.
Legal And Ethical Standards Alignment – Devs must adhere to laws i.e., GDPR & AI-specific guidelines and frameworks from IEEE or UNESCO. Ethical considerations, like impact of decisions on society, are integrated in design space
Environmental Sustainability – In large-scale AI models, efficient algorithms that minimize energy usage are a priority these days, especially for Devs working on optimization techniques to reduce computational overhead.