Trust and safety (T&S) has always been a foundational pillar for businesses worldwide, perhaps more so since the advent of the Internet and advanced technologies like AI. The challenges involved in ensuring online safety and security have shaped the T&S industry over the years. A DiMarket research report pegs the current T&S Safety Services market size to be around $15 billion, with a compound annual growth rate (CAGR) of 15% through to 2033.
One of the key trends shaping the T&S industry is the rising adoption of AI-powered solutions for enhanced online safety. However, concerns about the trustworthiness of AI solutions remain. Additionally, regulations such as the European Union’s Digital Services Act (DSA) make trustworthy AI an imperative. The three guiding principles of trustworthy AI systems are fairness, transparency, and accountability. Let’s understand trustworthy AI a little better.
why trustworthy AI?
Today, AI is driving many technological innovations across diverse industries, and trust is of paramount importance, especially in industries such as healthcare and finance, where decisions can have a life-changing impact on customers. Since AI is used to generate and analyze reports, aid decision-making, and enhance customer interactions, it can impact customer experiences and brand reputation. Biased AI systems can erode trust and impact business outcomes. Hence, it is essential to design and build ethical AI systems aligned with the principles of fairness, transparency, and accountability. Such systems are fair, safe, reliable, and resilient, fostering loyalty as they do not cause any unintended harm or discrimination.
- fairness: ensuring equitable outcomes
- transparency: building trust through understandability
- At the user interaction level, insights about purchase history and browsing patterns make users appreciate recommendations.
- At an algorithmic level, visual aids such as decision trees or heatmaps can provide a better understanding of how decisions are made.
- At a broader level, explaining the societal impact of AI systems ensures social transparency.
- Techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) provide insights into specific features that influence the AI decisions.
- accountability: owning AI’s impact
It is not uncommon for AI systems to be biased, as they often reflect human behaviour. Humans can be unfair and biased, and these discriminations can be passed on to AI systems during design and development. The same biases can also become an inherent part of the training data of AI models. It is essential to avoid these inequities while designing AI systems to avoid skewed outcomes and to ensure fairness for diverse populations.
Another aspect of fairness is explainability, which makes it easier to understand the logic behind AI decisions. Explainability ensures these systems are no longer black boxes, making AI outcomes interpretable.
fairness best practices
Fairness must be a dynamic goal that ensures mitigation strategies to address evolving concerns.
Bias detection tools and frameworks: Implement these tools and frameworks to detect and mitigate biases in AI models. They come with fairness metrics and bias mitigation algorithms that enhance fairness through corrective measures.
Audits: Implement continuous monitoring and conduct periodic third-party AI audits to find any hidden biases. Audits may involve the above-mentioned tools to test and recommend corrective actions for fairness.
Rebalanced data sets: Imbalanced training datasets with biased data representation majorly affect the fairness of AI systems. Curate the training datasets for fairness by ensuring a diverse demographic representation. Fairness-aware algorithms adjust data imbalances, as do training dataset audits. Techniques like adversarial debiasing used in these algorithms challenge the model to enhance its fairness outcomes.
Stakeholder engagement: Involve diverse groups in the design and development of AI systems to incorporate varied insights about potential biases early on in the process.
Transparency is a key factor impacting customer loyalty. And transparency in AI makes its operations understandable to users and builds trust. According to an industry survey report, 75% of the surveyed organizations felt that a lack of transparency could increase their customer churn.
transparency best practices
Transparency is about enabling meaningful human oversight to ensure trust while adopting AI solutions.
Explainable AI (XAI): Provide clear explanations about the input and output process to make the decisions of AI systems understandable. For example:
Documentation and reporting: Maintain detailed records during AI system development. Model cards and datasheets for datasets are emerging documentation standards used by AI practitioners to help stakeholders understand the intended and unintended uses of AI systems.
Accountability in AI is about ensuring proper mechanisms to hold those who own and build AI systems, responsible for their actions and outcomes, and provide redress.
accountability best practices
Accountability requires a mindset shift from viewing AI as a mere tool to recognizing it as a stakeholder in decision-making.
Governance structures: Establish clear roles and responsibilities for the development and deployment of AI systems. These should be aligned with globally accepted AI governance frameworks, such as the National Institute of Standards and Technology (NIST) and the Organization for Economic Co-operation and Development (OECD).
Impact assessments: Conduct regular assessments to evaluate the potential impacts of AI systems on individuals, groups, and society. The ISO/IEC 42005 standards can guide organisations in this endeavor.
Redressal mechanisms: Implement processes that allow entities to challenge and seek redress for AI decisions that adversely affect them. An AI ethics board comprising cross-functional experts that reviews AI deployments can significantly help this cause and ensure the human-in-the-loop best practice.
As AI systems embed into more walks of life, it is trustworthy AI that will differentiate the responsible ones from the laggard. Today, customers are more aware, and they demand fairness, transparency and accountability as much as the regulators and stakeholders do. It is only befitting that they get their dues even as the T&S industry continues to navigate the complexities of AI concerns.
how can Infosys BPM help?
Infosys BPM’s Trust and Safety Solutions encompass comprehensive T&S capabilities that help our customers proactively meet multiple challenges such as online safety, user privacy, regulatory compliance, and more. Our cutting-edge T&S offerings include content review and compliance, gaming experience, data annotation, and fraud and abuse prevention to name a few. Further, The Infosys Responsible AI Toolkit, an open-source offering, provides a collection of technical guardrails that integrate security, privacy, fairness, and explainability into artificial intelligence (AI) workflows.
