Building a responsible AI: How to manage the AI ethics debate

What is responsible AI?

As AI evolves, it has the potential to bring life-changing advances. So, before AI’s increasing momentum gathers even more pace, it is crucial to prioritize responsible AI development, which takes into account all potential societal impacts.

Responsible AI is an approach to developing and deploying artificial intelligence from both an ethical and legal standpoint. The goal is to employ AI in a safe, trustworthy and ethical way. Using AI responsibly should increase transparency while helping to reduce issues such as AI bias.

So why all the hype about “what is AI ethics”? The ethics of artificial intelligence are a huge challenge to humankind. Mindful and responsible innovation is not an easy concept in itself, but it is crucial to first grasp the question of what AI ethics are and integrate them into the core of the development and application of AI systems. In short, ethical AI is based around societal values and trying to do the right thing. Responsible AI, on the other hand, is more tactical. It relates to the way we develop and use technology and tools (e.g. diversity, bias).

Why is responsible AI important?

As AI becomes more business-critical for organizations, achieving responsible AI should be considered a highly relevant topic. There is a growing need to proactively drive fair, responsible, ethical AI decisions and comply with current laws and regulations.

Understanding the concerns of AI is the starting point for creating an ethical framework to guide its development and use. Any organization wishing to ensure their use of AI isn’t harmful should openly share this decision with as diverse a range of stakeholders as it can reasonably reach, along with consumers, clients, suppliers and any others who may be tangentially involved and affected.

Developing and applying AI along the principles of AI ethics requires transparency in decision-making processes and the development of actionable policies of AI ethics. With considered research, widespread consultation and analysis of ethical impact, coupled with ongoing checks and balances, we can ensure that AI technology is developed and deployed responsibly, in the interests of everyone, regardless of gender, race, faith, demographic, location or net worth.

What are the principles of responsible AI?

Confronting ethical concerns means engaging with their ramifications with foresight and commitment. It’s vital to view AI’s ethical dimension not as an obstacle but as a conduit to lasting and sustainable tech progress. That’s why embedding responsible AI principles is essential to its evolution in a direction that benefits all.

While there isn’t a fixed, universally agreed-upon set of principles for AI ethics, several guidelines emerge. Some key principles of AI ethics are:

• Fairness: Datasets used for training the AI system must be given careful consideration to avoid discrimination.
• Transparency: AI systems should be designed in a way that allows users to understand how the algorithms work.
• Non-maleficence: AI systems should avoid harming individuals, society or the environment.
• Accountability: Developers, organizations and policymakers must ensure AI is developed and used responsibly.
• Privacy: AI must protect people’s personal data, which involves developing mechanisms for individuals to control how their data is collected and used.
• Robustness: AI systems should be secure – that is, resilient to errors, adversarial attacks and unexpected inputs.
• Inclusiveness: Engaging with diverse perspectives helps identify potential ethical concerns of AI and ensures a collective effort to address them.

 

Promoting responsible AI practices

These principles should help to steer considered and responsible decision making around AI. In order to transition from theory to practice, organizations must create actionable policies of AI ethics. Such policies are crucial in weaving ethical considerations throughout the AI life cycle, ensuring integrity from inception to real-world application.

While organizations may choose different routes to embed responsible AI practices into their operations, there are a few AI best practices that can help implement these principles at every stage of development and deployment.

When deciding how to establish AI ethics, companies should:

• Foster collaboration across all disciplines, engaging experts from policy, technology, ethics and social advocacy to ensure multifaceted perspectives
• Prioritize ongoing education on AI best practices at all levels to maintain awareness and adaptability
• Implement AI ethics throughout the technology’s design, building them into AI solutions from the ground up
• Establish clear oversight mechanisms, such as ethics committees or review Boards, to monitor compliance and guide ethical decision making
• Protect end-user privacy and sensitive data through strong AI governance and data usage policies
• Encourage transparency in AI processes, enabling accountability and trust from stakeholders and the public

Keeping up with AI best practice

To keep your AI system trustworthy, it’s important to focus on three key areas: feeding it good, diverse data; ensuring algorithms can handle that diversity; and testing the resulting software for any mislabelling or poor correlations.

Here’s how to achieve this:

• Design for humans by using a diverse set of users and use-case scenarios, and incorporating this feedback before and throughout the project’s development.
• Use multiple metrics to assess training and monitoring, including user surveys, overall system performance indicators, and false positive and negative rates sliced across different subgroups.
• Probe the raw data for mistakes (e.g. missing values, incorrect labels, sampling), training skews (e.g. data collection methods or inherent social biases) and redundancies – all crucial for ensuring responsible AI principles of fairness, equity and accuracy in AI systems.
• Understand the limitations of your model to mitigate bias, improve generalization and ensure reliable performance in real-world scenarios; and communicate these to users where possible.
• Continually test your model against responsible AI principles to ensure it takes real-world performance and user feedback into account, and consider both short- and long-term solutions to the issues.

Responsible AI: examples of success

By integrating responsible AI best practices and principles, we can ensure we end up with generative AI models that ultimately enrich our lives while keeping humans in charge. As we steadily transition towards a more responsible use of AI, numerous companies have already succeeded in creating AI-powered products that are safe and secure.

Let’s take a look at some responsible AI examples:

• The Fair Isaac Score, by analytics software firm FICO, is a credit scoring system that uses AI algorithms to assess creditworthiness. FICO maintains responsible AI practices by regularly auditing its scoring models for bias and disparities based on mathematics instead of subjective human judgement.
• Healthcare startup PathAI develops AI-powered diagnostics solutions to aid pathologists in diagnosing diseases. To ensure the safe and responsible use of AI in its software, the company validates the accuracy and reliability of its algorithms through rigorous clinical testing and peer-reviewed studies.
• With its people-first approach, IBM’s Watsonx Orchestrate is revolutionizing talent acquisition. This AI solution for HR and recruitment promotes fairness and inclusivity in the hiring process by generating diverse pools of candidates, using fair assessment criteria, and prompting managers to incorporate diverse perspectives in the interview process.
• Ada Health provides users with personalized medical assessments and advice. The AI-powered chatbot safely handles the diagnosis and screening of common conditions like diabetic retinopathy and breast cancer. AI best practices are ensured through transparent disclosure that users are interacting with an AI chatbot.
• Using a constellation of satellites, Planet Labs is pioneering the use of AI in satellite imagery, transforming how we monitor the environment, analyse climate patterns and assess agricultural yields. By collaborating with environmental organizations and policymakers, the company ensures AI best practices are embedded in its model.

The standards approach

As we advance towards responsible AI, every corner of society needs to engage and be engaged. ISO, in collaboration with the International Electrotechnical Commission (IEC), is keeping pace with this pursuit, crafting International Standards that safeguard and propel the principled application of AI technology.

In shaping ethical AI, the world’s governments, organizations and companies need to embody these values, ensuring that their pursuit of innovation is accompanied by ethical responsibility. International Standards will help to establish a high watermark of ethics in AI, consistently guiding the best practice in this transformative industry.

A commitment to responsible AI is not a one-time act, but a sustained effort involving vigilance and adaptation. However, organizations should be aware that this commitment not only guides AI to align with common welfare, it also opens doors to its vast potential.

• ISO/IEC 42001:2023AI management systems

Reaping the rewards

There is every reason to be optimistic about a future in which responsible AI enhances human life. It is already making game-changing strides in healthcare, education and data analytics. It has the capacity to supercharge human resilience and ingenuity at a time when we – and the planet – need it most. Rooted in ethical design, it can offer us a symbiosis of technological innovation and core human principles, culminating in an inclusive, flourishing and sustainable global community.

Responsible AI represents a comprehensive vision to mirror society’s ethical fabric within machine intelligence. It signifies a pledge to forge AI systems that uphold human rights, privacy and data protection. Through this lens, every AI initiative undertaken becomes a stepping stone towards a future where technology not only empowers, but also respects and enhances, the human condition.