5 ethical AI considerations to future proof your business

The main ethical challenges of AI fall into four broad categories: digital amplification, discrimination, security and control and inequality

With greater scrutiny of tech practices and calls for transparency, businesses must manage the deployment of smart AI while ensuring privacy safeguards, preventing bias in algorithmic decision-making, and meeting guidelines in highly regulated industries. In this article, I look at five ways leaders can future-proof their businesses against these risks.

Staying ahead of regulatory changes

Regulating AI is a multifaceted and difficult challenge, and as a result, the regulatory landscape is a consistently evolving environment. However, the issue of unethical and biased AI is becoming critical as organizations are increasingly relying on algorithms to support their decisions – and we will undoubtedly see the ramp-up of regulatory scrutiny in the coming years as a result. To avoid the consequences of financial and reputational damage from unethical AI, organizations will need to get ahead of the curve.

This will mean developing a comprehensive AI risk framework to articulate and maintain ethical standards. Unfortunately, at our current pace of innovation, many companies lack visibility into the risk of their own models and AI solutions, and not all algorithms require the same scrutiny. When considering future regulatory changes, organizations should ensure they tailor their framework to their industry; for example, regulators are likely to exempt low-risk AI systems that pose no risks to human rights and safety, while financial and healthcare industries will require rigorous guardrails.

Prioritizing accountability and explainability

Moving forward, businesses and academics will be called on to keep records explaining their algorithmic systems, including the data, processes, and tools behind them. Because algorithms can be so complex, companies should be overzealous when explaining what data is being used, how it’s being used, and for what purpose.

Accountability provides the guiding policies, standards, and checklists that we need to enforce throughout the lifecycle of AI initiatives to stay ahead of regulatory standards. One of the critical components of accountability is ensuring AI applications are explainable; the AI design and the development team should be open about why and how they are using AI. Equally, the customers of the applications should understand the behavior of the algorithms, through improved interpretability or intelligibility. Such explainability helps organizations to achieve a diverse set of objectives to mitigate unfairness in AI systems, help developers debug their AI systems, and build trust.

Leading AI from the top

AI is increasingly being used for solving business problems and achieving the boardroom’s long-term goals. Any ethical AI considerations should therefore be led from the very top, with the CEO setting the strategic vision for what constitutes the responsible use of AI. They can’t achieve this alone; board members, executives and departmental heads should collaborate to form a senior-level working group that is responsible for driving AI ethics within the business.

From my experience working with clients on responsible AI, I’ve seen most success when this group is made up of cross-functional skills including legal and compliance, technology, HR, and operations. Collectively, these experts can work together to understand regulatory frameworks within their industry and the implications on their business to form the basis of a responsible AI business strategy.

Embed within the culture

Ethical AI means embedding AI ownership and accountability into all teams, ensuring employees fully understand AI and how it relates to their roles. We believe that everyone in the organization – from HR, marketing to operations – has an equal right to be educated and empowered to leverage AI technology for personal and professional use. It is leadership’s task to upskill employees to understand the company’s AI ethics framework and to understand how to elevate ethical concerns to the committee when issues arise.

With the growing importance of ethical AI comes a shift in defining, measuring, and incentivizing success, and there may need be a readjustment in personal KPIs to encourage employees to play their role in maintaining responsible algorithms and calling out bias when they see it.

Turning ethical AI to a competitive advantage

Stakeholder capitalism will be a key driving factor of every organization in the future. Keeping employees, customers, and suppliers motivated and inspired is critical to ensuring that those participants continue to deliver returns to shareholders and ultimately ensure long-term corporate prosperity. But these stakeholders also require a clear understanding of company purpose and values – not just financial goals and objectives – and this includes ethical AI. If businesses take the chance to develop AI algorithms with transparency and accountability, they can turn this to a competitive advantage. When designed, governed, and implemented correctly, responsible AI can improve corporate social responsibility (CSR) by mitigating adverse or negative impacts on society, helping create trust and maximizing long-term value creation.

Ethical AI is still in its infancy for most businesses. However, leaders need to ensure they are governing AI in a responsible and moral manner to overcome this new wave of challenges. Forward-thinking companies can use their strategy as a competitive advantage to be able to better win in the new and evolving market.

This article was written by David Semach, EMEA Head of AI and Automation at Infosys Consulting