AI and Ethics: Striking a Balance in Software Development

Dear LinkedIn Community,

Welcome to the twelfth edition of Velvetech’s IT Talks!

As you know, AI has been a hot topic in recent years and especially in the last couple of months. Yet, as artificial intelligence continues to get the spotlight, so do the questions surrounding its ethical concerns.?

As AI becomes more pervasive, it is crucial to consider the ethical implications of its use in software projects. After all, the consequences of bias, copyright infringement, and the like can be rather grave.

So, let’s take a look at some of the common questions surrounding AI ethics.

Q1: What are the trade-offs between AI transparency and performance or accuracy? How can we balance these competing priorities?

It’s true, the more complex the AI system is, the harder it is to provide both transparency and accuracy. Primarily because the black-box nature of certain AI models limits their explainability and transparency. Yet, both of these characteristics are imperative for building trust in AI-powered software, ensuring that they operate ethically, and allowing people to comprehend and actually validate the results they produce.

So, balancing these competing priorities really depends on the industry your company operates in. For instance, medical institutions are unlikely to approve an AI model that they do not understand and can’t verify. On the other hand, organizations like hedge funds might accept AI-enabled suggestions and tips if they lead to profitability even if they do not understand how the platform arrived at a particular recommendation.

Q2: What are the potential risks associated with biased AI and how can we mitigate those risks?

Biased AI can have drastic negative impacts on individuals and businesses alike. Many of us have heard of Amazon’s former AI-based recruiting tool that showed bias against women. That’s just one popular example.

In truth, biased models can lead to discrimination against certain groups of people based on their race, age, religion, gender, and so on. Naturally, this can produce inaccurate results since instead of optimizing for a certain metric that leads to efficiency or profitability, the AI is delivering biased, discriminatory suggestions.

So, biased models can lead to poor decision-making due to imbalances within the data. For instance, a trading bot may develop a preference for cryptocurrency portfolios since the majority of the people whose strategies it studied upon had a large amount of these types of digital assets.

Bias risks can be mitigated by balancing the data, and carrying out training on diverse data sets that include examples from different groups of people and varying scenarios. Additionally, it’s a good idea to conduct audits with specialists who have experience in using an AI model in a particular domain.

Q3: How can we address bias that is discovered in our AI systems after deployment?

In short, you can correct the model by examining the imbalance in the data that was used for training and essentially retraining the model. Likewise, you can release iterative updates all the while monitoring the effect of any changes. That way, you’ll be able to fine-tune the model toward more balanced solutions.

Addressing bias in AI requires collaboration between data scientists, domain experts, and various stakeholders. That is the only way to ensure that the system’s output is fair and actually useful.

Ask Your Question to Our AI Experts

Thank you for checking out our newsletter once again. The subject of AI ethics and bias is an important one, and we’d love to hear of any experience you may have had with it. So, don’t hesitate to share in the comments below.

Or, if you’ve got an AI question for Velvetech’s specialists, leave it under this post as well. Our experts will answer it shortly.