AI can see clearly now: Why transparency results in ethical and fair AI systems

Although artificial intelligence has proved its ability to reshape industries, redefine customer experiences and reimagine business operations, it also carries inherent risks. And though robots haven’t overtaken the world as foreshadowed time and time again by science fiction movies, there’s a really real threat to businesses of AI going awry.

One among the important thing components in helping ensure AI is behaving the best way it’s intended to is transparency. AI can’t be operating in a black box by which nobody understands how its making decisions – that’s the way you run into issues similar to unintentional discrimination and bias.

At its core, transparency in AI refers to the power to grasp and trace how AI systems make decisions. It’s about making the inner workings of AI algorithms clear to humans, particularly those that use, regulate or are affected by them.

These systems learn from vast amounts of information, often making decisions in ways in which are usually not inherently clear, even to their creators. If an AI algorithm operates because the aforementioned black box, we call this opaque AI – we will’t see it or understand it. AI systems can inadvertently perpetuate and amplify biases of their training data. Transparency allows for the examination and understanding of how these biases occur, resulting in more ethical and fair AI systems.

Law and ethics: Why transparency is critical

Transparency builds trust with consumers, employees and stakeholders. When users understand how and why an AI system makes decisions, they usually tend to trust and accept it. But, depending on the industry, the extent of AI opacity varies. For instance, in highly regulated industries, transparency is paramount for legal and regulatory compliance. Not complying could mean serious implications and dear fines that might upend a business.

The regulatory environment is usually much slower than the speed of innovation and there’s a chasm between the governing strategies of assorted regions. For instance, within the U.S., there’s the likelihood that 50-plus different privacy laws could govern AI depending on the legislative appetite in each state, whereas in Europe, there’s a consensus approach between EU member states. This makes things very complicated, depending on a business’ location and where there their customers are, and operating transparently means higher compliance with local regulations.

If regulatory compliance doesn’t compel businesses to be transparent, what is going to? The reply should be ethics. If transparency is an element of a company’s core values and is incorporated into AI strategies, they’re demonstrating empathy for purchasers and stakeholders since the business prioritizes fairness, respect and privacy, which is in the perfect interest of us all.

Challenges with achieving transparency in AI

Developing more explainable AI models is the core tactic for achieving transparency, but that’s typically easier said than done. Many view AI models and algorithms as a “secret sauce,” that if exposed can be tantamount to ceding competitive advantage: Algorithms could be classified by some as mental property.

There’s also a relationship between opacity and predictive power. Opaque models are sometimes more powerful. As a marketers, that is an analogous comparison to the connection between audience reach and accuracy in data-driven campaigns. The broader the audience is, the less relevant the messaging may be, whereas if the audience is more granular, the messaging may resonate more despite reaching less people. It’s a tradeoff we must analyze against our goals and budgets.

With regards to statistical and machine learning models, they vary from easy and transparent to complex and opaque. Some AI models are incredibly complex, similar to deep neural networks. Some examples of technology that uses DNNs are voice assistants similar to Siri and Alexa, advice algorithms like those utilized by Netflix and YouTube, language translation services, and self-driving cars.

Simplistic models include linear regression and decision trees. A call tree could be made on an easy piece of paper by someone who isn’t an information scientist, because it’s extremely easy to see the choice path toward an end result. Decision trees could be used for loan approval processes, while linear regression is utilized in credit scoring and real estate pricing.

There’s a tradeoff between accuracy and opacity. Netflix recommendations are going to be rather a lot more accurate versus a human using a call tree to find out a loan approval. And though there’s an algorithm that’s widely used for real-estate appraisals, the method varies based on aspects outside the model, including who’s performing the evaluation. This all results in challenges find the proper balance to realize true transparency while also ensuring accuracy.

Strategies for enhancing transparency

Despite these challenges, there are strategies that may also help enhance organizational AI transparency. One is to integrate transparency considerations into your AI systems from the start of the event process.

This goes hand-in-hand with creating an organizational culture that strives for transparency. Accountability ought to be shared – not only taken on by technologists, but from functional areas similar to marketing, operations, sales, customer support and beyond to bolster its importance and make it a part of company culture.

Moreover, continuous monitoring by a human to oversee AI decisions and performance is important in maintaining transparency. If there’s an issue or bias emerges, a human auditor can catch it before it’s reinforced again and again. 

Businesses also needs to clearly state and publicize how data is collected, used, processed and handled, since AI systems are only as fair and accurate as the information fed into them. Not only does this enhance transparency, it also enhances consumer trust. Most organizations that handle consumer data post their privacy policies online, and if we do the identical for AI governance policies, we will further construct trust and foster adoption.

Setting industry standards can be essential and achievable. This requires organizations to return together and develop a framework for responsible AI best practices, or establish agnostic organizations that develop and maintain standards, offer benchmarking and conduct research to measure adherence to such frameworks.

As AI becomes increasingly integrated into enterprise operations and the on a regular basis lives of consumers, transparency will probably be critical to unlocking its full potential. It’s central to constructing consumer trust, ensuring fairness for marginalized groups, and meeting regulatory standards across industries. While technologists are still solving challenges that contribute to the opacity of AI algorithms, we will concurrently come together to create accountable cultures, best practices and agreed-upon frameworks in pursuit of a more transparent and ethical future.

Tara DeZao is director of product marketing, adtech and martech, at Pegasystems Inc., which develops software for customer relationship management and business process management. She wrote this text for SiliconANGLE.

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