How can software engineers prevent AI bias in their work?

An AI model is a sophisticated software system that leverages diverse data and historical statistics to make predictions. To ensure that commercial considerations do not solely drive these models but are also rooted in principles of integrity and value, software engineers need to contemplate their development from a holistic perspective. 1. I lived in the UK and realized that every resident living on a specific street would have their credit card application declined. I realized this when a different address approved the application 2. I once realized that a picture scan model was biased and used skin color to influence a model outcome. A model that uses gender, color, income, race, ethnicity & or religion to influence is a biased model

Ask three key questions a. Is your data ready for prime time AI ? Ans: No, got it - invest in Data Quality and Labelling. This will also help Model Fine Tuning later. b. Do you have a preference of using one LLM/Algorithm over the other for your AI/ML project? Can you explain precisely why ? Ans: If the preference of using a LLM/Algorithm over the other is more than just a business oriented fruitful business partnership but because certain LLM's/Algorithms are more suited for a specific task. c. Do you have a well defined MLOps process that can be easily fine-tuned to meet changing market dynamics, regional data residency, security and governance rules ? Ans: No, ok its never too late to invest in building a mature MLOps practice.

In my experience there are 3 ways bias finds it’s way into production AI systems; - Biased data (Easiest to counteract) - Biased business aims built into model build (Difficult to counteract) - Biased builders, MLOps implementers, data scientists etc (Very difficult to both quantify and counteract in culturally immature environments) When the average person thinks of bias they may consider it through a non-personal lens - that’s a mistake when AI is an extension of ourselves rather than some novel, super intelligent being. Design your AI data, models, processes and Ops to encapsulate the fundamentals of responsible and ethical AI/AI engineering at each stage.

In healthcare and education, countering AI bias is vital. My approach: 1. Data Integrity Assurance: Auditing data for integrity. 2. Data Enrichment Strategies: Enhancing data richness. 3. Algorithm Selection Expertise: Precise algorithm selection. 4. Algorithm Transparency: Exploring algorithm logic. 5. Fairness and Accountability Assessment: Ensuring ethical AI. 6. User-Centric Validation: Rigorous user testing and feedback.

A very quick way to test out the potential for bias, lack of ethical considerations or immature AI responsibility is to form cross-business or cross-organisational Alpha testers for your AI models and systems. Give them an overview of what the AI model or system is intended to do, and then ask them to test it and feedback you from their user perspective how it’s going. Depending on how psychologically safe your environment is, you could introduce anonymous feedback systems. Consider asking your testers to score your AI model/system against a objective responsible/ethical AI framework. By introducing the potential for your bias to be unearthed & challenged, you are taking a giant step towards responsible and ethical AI engineering.