Responsible and trusted AI

Key principles for responsible AI: accountability, inclusiveness, reliability and safety, fairness, transparency, and privacy and security. These principles are essential to creating responsible and trustworthy AI as it moves into mainstream products and services.

Accountability

Accountability is an essential pillar of responsible AI. The people who design and deploy an AI system need to be accountable for its actions and decisions, especially as we progress toward more autonomous systems.

Organizations should consider establishing an internal review body that provides oversight, insights, and guidance about developing and deploying AI systems. This guidance might vary depending on the company and region, and it should reflect an organization's AI journey.

Inclusiveness

Inclusiveness mandates that AI should consider all human races and experiences. Inclusive design practices can help developers understand and address potential barriers that could unintentionally exclude people. Where possible, organizations should use speech-to-text, text-to-speech, and visual recognition technology to empower people who have hearing, visual, and other impairments.

Reliability and safety

For AI systems to be trusted, they need to be reliable and safe. It's important for a system to perform as it was originally designed and to respond safely to new situations. Its inherent resilience should resist intended or unintended manipulation.

An organization should establish rigorous testing and validation for operating conditions to ensure that the system responds safely to edge cases. It should integrate A/B testing and champion/challenger methods into the evaluation process.

An AI system's performance can degrade over time. An organization needs to establish a robust monitoring and model-tracking process to reactively and proactively measure the model's performance (and retrain it for modernization, as necessary).

Fairness

Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system's decisions don't discriminate against, or express a bias toward, a group or individual based on gender, race, sexual orientation, or religion.

Microsoft provides an AI fairness checklist that offers guidance and solutions for AI systems. These solutions are loosely categorized into five stages: envision, prototype, build, launch, and evolve. Each stage lists recommended due-diligence activities that help minimize the impact of unfairness in the system.

Transparency

AI systems should be understandable by those who use them. The processes and decisions made by AI should be explainable to a certain degree, allowing users to comprehend how and why certain outputs are generated.

Privacy and security

AI should be designed to protect personal and sensitive information, ensuring that data is used appropriately and in line with relevant legislation (such as GDPR).

Personal data needs to be secured, and access to it shouldn't compromise an individual's privacy. Azure differential privacy helps protect and preserve privacy by randomizing data and adding noise to conceal personal information from data scientists.

Ignoring Responsible AI Principles can lead to AI system failures and result in public rejection or bans.

Legal and Regulatory Consequences

• Regulatory Penalties: If the AI product violates data protection laws, the developing company could face significant fines and legal action.
• Lawsuits: Affected individuals or groups may file lawsuits against the company for damages caused by the AI system, such as discrimination or privacy violations.

Reputational Damage

• Public Backlash: If the AI system is found to be biased, invasive, or otherwise unethical, public opinion can quickly turn against the company, leading to a loss of trust and credibility.
• Consumer Boycotts: Users may choose to boycott the company's products or services if they believe the company is not upholding responsible AI practices.

Social and Ethical Implications

• Discrimination: AI systems that are not developed with fairness in mind might propagate biases, leading to discrimination against certain groups in critical areas like employment, healthcare, and law enforcement.
• Privacy Violations: Without strong privacy safeguards, AI could intrusively collect and misuse personal data, infringing on individuals' rights to privacy.
• Harm to Individuals: In the worst scenarios, irresponsible AI could lead to real harm, such as through errors in autonomous vehicles or medical diagnostics.

Economic Implications

• Loss of Business: Clients and partners may terminate contracts or refuse to collaborate if the AI product is considered unethical or harmful.
• Increased Costs: The business might incur additional costs to address the fallout, including rectifying the issues, legal fees, and investment in public relations to manage reputational damage.

Technical Consequences

• Lack of Trust in the System: Users may be reluctant to use the AI system if they feel it cannot be trusted to make fair and reliable decisions.
• Poor Adoption: If the AI product is seen as irresponsible or harmful, this could impair adoption rates, as end users or other stakeholders avoid engaging with the product.

Overall Impact on Society

• Erosion of Trust in AI: Irresponsible use of AI can lead to a broader public distrust in AI technologies, potentially stifling innovation and the adoption of otherwise beneficial systems.
• Exacerbation of Inequalities: Biased AI systems can further entrench existing social and economic inequalities.

Impact on the AI Industry

• Stricter Regulations: A high-profile failure of an irresponsible AI system may prompt governments to impose stricter regulations on the AI industry, affecting innovation and deployment timelines.

Future Business Sustainability

• Impediment to Future Business: Negligence in the present could hamper future business opportunities, as potential clients may be cautious about the company's commitment to ethical practices.

Creating responsible AI is not just a moral imperative; it's also a business necessity in an increasingly aware and regulated global environment. Companies that disregard responsible AI principles may find themselves facing not only short-term consequences but also long-term obstacles that could have been mitigated through a more thoughtful approach to AI development and deployment.