Strategies To Defend Against AI Algorithmic Bias

Strategies To Defend Against AI Algorithmic Bias

April 11, 2024

Bias in data systems isn’t new. But when artificial intelligence (AI) entered the scene, bias followed. How can you prevent AI bias? It starts with a solid foundation of reliable information and lots of oversight.

Think of your AI as a pinch hitter, helping you bring your best game to the competition. A pinch hitter becomes the best batter with intensive training from a coach who uses precise, proven techniques to hone their skill. To make your AI a star on your team, you need trusted experts to teach it the ropes and people to supervise it. 

Let’s explore how to successfully and responsibly use AI at your business:

  • Scrutinize the training data, sources and methodology before integrating AI tools into your business.
  • Develop a vision and risk assessment before launching your AI. You need a clear understanding of how AI will help your business.
  • Put limits on your AI tools, especially in decision-making tasks. Don’t allow unmonitored automation. Human controls are critical.
  • Create an AI oversight committee that stays current on all things AI.

Algorithmic bias

Algorithmic bias refers to systematic and repeatable errors in a computer system that produce unfair outcomes. For example, algorithmic bias might favor one group of users over another. It typically refers to a bias built into the data used to train an AI system, leading it to make discriminatory decisions or recommendations. Bias can happen during data collection, data selection or the AI training phase.

Consider these real-life algorithmic biases:

  • Speech recognition that has difficulty understanding diverse accents or languages because it wasn't trained on them
  • Health care AI that makes diagnostic errors because it wasn’t trained on large, diverse groups of people
  • Facial recognition software that can’t accurately identify facial markers across multiple races or genders
  • Hiring software that rejects candidates based on gender because its training data was mostly one gender
  • AI that denies loan and credit applications based on gender because its training data was skewed toward one gender
  • Marketing algorithms that reinforce and perpetuate human biases, like advertising investment services primarily to men

An unimpeded AI can quickly create a business liability and public relations disaster.

Who’s liable?

AI gets its data from many sources. It also undergoes layers of training. 

The legal gray area is who’s liable for an AI’s biases: programmers, AI trainers or business owners? The answer is unclear, and it could take years before court decisions and regulatory bodies figure it out.

Until laws catch up with tech, you should assume liability and take proactive measures to keep your AI systems as bias-free as possible. A clearly defined process for vetting your AI vendor, datasets and training methods will be critical to your defense in a lawsuit.

How do you insure AI?

AI is a computer technology, so it’s insured that way. You can purchase cyber liability insurance to transfer your liability, but you’ll need to implement best practices to safeguard against AI algorithmic bias. In fact, you’ll probably need an AI governance policy to secure cyber liability insurance.

Establish an AI governance policy

AI governance might sound techie, but it’s like any other business policy you’ve created. Create an AI governance board responsible for developing a testing, implementation and rollout strategy. Ask yourself why you need an AI system and what operations it should fulfill. Make sure it fits your business’s brand and mission.

Create an AI compliance committee and fill it with people representing your company’s diverse and cross-functional roles. AI should reflect your values, goals and legal requirements. Assign roles and responsibilities, and create a meeting schedule. You might want to meet more often during implementation and rollout, but also make time to reconvene and assess how the AI is working across the business.

Ensure your AI is traceable

Traceability is knowing where your AI tools and their training data originated. Traceability can mean better control over your AI’s accuracy, functioning and overall performance. 

Look for reputable AI vendors that use large-scale data they can source. Current AI models typically perform better on large datasets. If your generative AI is scrutinized, you’ll want to be able to defend its sources. It’s like writing a research paper. You need references to support your claims.

Before signing with an AI vendor, get your lawyer to review any contracts, including compliance issues, training data sources, and intellectual property or copyright issues. And make sure you know who can update your data after you acquire your AI.

Make your AI system explainable

Explainability is knowing how your AI operates, similar to knowing how a product is assembled on your facility’s assembly line. Explainability ties back to traceability. This level of understanding empowers you, assuring everybody the AI is playing fair regardless of its role. Your AI system should not be a mystery to you or your customers, especially if you’re involved in a lawsuit.

Implement AI training and ethics

Think of this step as putting up guardrails for your AI, showing it how to meet your business objectives. Work with your various departments to ensure the AI understands its job and limits. Ensure your AI knows when to seek assistance from a human and when it’s OK to decide independently.

You’ll also need to define your business morals and company culture for your AI. It’s all about making your AI reflect your business’s standards, whether it’s handling customer data or ensuring the quality of your assembly line products.

Monitor your AI

Monitoring your AI ensures it performs reliably, checks bias and behaves as you intended. Even if your AI manages customer relationships or provides safety recommendations for your factory, you’ll know it’s doing its job properly. 

Monitoring is an important part of the oversight process. You can assign monitoring to one human or several, depending on your operations. It’s also a good idea to involve your AI committee in the feedback process.

Audit your AI to prevent bias

Auditing is as necessary for AI as food inspections are for food processing plants. It’s not a one-and-done thing, either. AI auditing is part of a continuous cycle of data integrity.

The White House has released recommendations on Algorithmic Discrimination Protections as part of its Blueprint for an AI Bill of Rights. The following is a summary of the recommendations for AI system designers, but you could also use it as a guide for your AI systems audits:

  • Assess for equity. Before creating automated systems, check if they will treat everyone fairly. Pay special attention to underrepresented communities to ensure these systems don’t harm or discriminate against them.
  • Use representative and robust data. The information used to build a system should reflect the real world and its various communities. This avoids biases and harm.
  • Guard against proxies. Don’t use demographic details when creating automated systems since that could lead to discrimination. Even seemingly unrelated factors could unintentionally reflect demographic attributes, contributing to bias.
  • Ensure accessibility. Develop systems so everyone, including people with disabilities, can use them easily.
  • Assess disparities. Thoroughly check algorithms to ensure they don’t treat different groups of people differently. These checks should cover various demographic categories.
  • Mitigate disparities. If you find unfairness during your checks, remove or reduce it. If you uncover a severe disparity, you might need to reconsider the system entirely.
  • Monitor your system. Keep an eye on automated systems so you can find and fix any biases. 

You can hire an AI inspector to check the quality of your AI algorithms. They will consider factors like traceability, reliability and data quality to ensure the AI system was trained without bias or discrepancies. 

Professional AI auditing firms use advanced tools and human judgment to audit vast data. They can validate your AI training processes and ethical guidelines. If you outsource your AI to a third party, you can engage independent AI auditors to ensure they operate your AI within your business goals and ethical norms. 

Consider using an AI auditor before purchasing your AI and after as an ongoing housekeeping measure. Proactive audits can help catch biases before they become pervasive or cause an incident.

Safeguard your AI with cybersecurity

You’ll need strong cybersecurity protocols for your AI. Cybersecurity protects your data from hackers and maintains traceability and explainability for you. With cybersecurity controls like multifactor authentication, you can control who can access your proprietary data. You can also prevent threat actors from infecting your AI with malicious data or misinformation. 

Remember, threat actors can also be disgruntled employees or competitors, so a zero-trust cybersecurity approach is best.

AI, responsibly

Check with your lawyer and insurance agent about liability and risk mitigation. Whether you’re providing customers with a memorable dining experience or housing assistance, your AI tools should act fairly and equitably.