What are some best practices for avoiding algorithmic bias and discrimination?

With thoughtful data collection, representative sampling, rigorous statistical analysis, and extensive testing, algorithms can be made more fair, safer, and more beneficial for all groups in society. However, this requires making ethical considerations central throughout the machine learning pipeline, from problem formulation to model deployment. I would advise technology leaders to assemble diverse teams to critically assess each model iteration, proactively search for biases, and make algorithmic fairness an organisational priority on par with performance metrics.

Biases mostly creep into the algorithms which are mostly data driven due to one or all of the following reasons : 1. Historical data that was pooled from a certain geography or demography. 2. Lack of diversity in the team writing the algorithm. 3. General human biases towards certain topics. 4. Representation of the RLHF in LLMs , quality of feedback and checks and practices enforced to prevent bias. 5. Incomplete or under represented training and validation data Strategies: - Formulating a Fairness detection algorithm for validation -Trading off accuracy for fairness - Diversity of Human Feedback, Formalizing HF selection - Inclusion of human validation with a certain "ethics" cookbook - Diversity by Design during inception

I would emphasise that algorithm ethics is not just about avoiding direct harms, but promoting human flourishing. Systems must be designed to empower and uplift users, opening new possibilities for those who have historically been marginalised. This requires foresight into how algorithms may influence behaviours and values over time. Rather than an afterthought, ethics must be woven into the algorithmic process from the start. Impact assessments, equity audits, and red teaming exercises focused on preventative safety can uncover risks early when they are easiest to address. By broadening our vision of success beyond efficiency alone, we can build not only “smarter” systems, but kinder ones.

I would caution against over-reliance on tweaking datasets and models: this treats symptoms rather than causes. We must recognise that bias encoded in data, or perpetuated through analytics, originates from and reinforces long-standing societal inequities. Rather than strive for illusory neutrality, I advocate sincere engagement with affected communities to understand needs, shift power dynamics, and co-create solutions. Only by connecting our technical pursuits to core values of empowerment, equity and human dignity can we realise technology's emancipatory potential.

To enrich your journey in understanding algorithm ethics and social impact, consider diving into a diverse range of resources. For a comprehensive theoretical foundation, "Oxford Handbook of Ethics of AI" edited by Dubber, Pasquale and Das offers a multidisciplinary perspective. To grasp the real-world implications, "Hello World: Being Human in the Age of Algorithms" by Hannah Fry provides an accessible and engaging narrative. For a deeper legal and policy-oriented viewpoint, delve into "The Black Box Society" by Frank Pasquale. Joining online courses from platforms like Coursera or edX, particularly those focusing on AI and ethics, can also be incredibly beneficial.