Ethical Concerns of Using Customer Data in AI Systems
A look at privacy, bias, transparency and other issues retailers must consider when collecting and applying customer insights with AI.
Artificial intelligence and machine learning have become powerful tools for enhancing customer experience. By collecting and analyzing vast amounts of customer data, companies can gain valuable insights into preferences, behaviors and needs. This information can then be applied through AI systems to personalize product recommendations, target digital advertisements, optimize service workflows, and more.
However, harnessing the power of customer data and AI also introduces important ethical considerations around privacy, bias, transparency and fairness. As companies look to new technologies to better understand their audiences, it is crucial they do so in a responsible manner that respects individuals and promotes justice.
Privacy and consent
One of the main concerns around large-scale customer data collection is privacy. Many people may not fully understand how their information is being gathered, shared and applied. Companies need transparent policies around what specific data elements they track, how it is stored, who has access to it, and what it is used for.
According to a 2021 survey, 81% of consumers said they feel they have lost control of how personal data is collected and used by companies. Further, 59% do not trust companies to ethically use AI based on personal data. [1] This highlights the need for open communication and consent around data practices.
According to a 2021 survey, 81% of consumers said they feel they have lost control of how personal data is collected and used by companies.
Customers should also have the ability to access any data a company has on them, as well as the option to correct inaccuracies or opt-out of certain data uses. Opt-in consent, rather than blanket assumptions of approval, is important for building trust. Companies relying on sensitive personal details, like health, location or financial records, have an even higher responsibility to protect privacy.
Algorithmic bias
AI systems are only as unbiased as the data used to train them. If that data reflects existing social biases in unfair or discriminatory ways, the resulting algorithms may systematically disadvantage certain groups. For example, models using historical customer records could potentially learn patterns that disadvantage minority customers without meaning to.
One study found that around 3 in 4 AI systems exhibited some level of bias favoring certain groups over others. [2] Companies must proactively assess their data and algorithms for unfair biases related to attributes like gender, race, age, disability or socioeconomic class. Where biases are found, steps need to be taken to rebalance data or tweak models before deployment. Ongoing bias monitoring and response plans are also advisable to catch issues that emerge over time.
Lack of transparency
The complex algorithms behind modern AI systems can be difficult for outsiders to fully understand, audit and scrutinize. This lack of transparency introduces risks if decisions are made or harms occur without a clear explanation.
Further, 59% do not trust companies to ethically use AI based on personal data.
In one survey, 78% of consumers said being able to understand how an AI system makes decisions is important to them. However, only 24% of AI practitioners said their organization can actually explain their AI’s decisions and biases. [3] Companies should strive to communicate the general types of factors that go into their AI-driven customer decisions, even if full algorithmic details remain proprietary. Users also deserve easy-to-access explanations around any significant automated decisions that impact them, such as credit denials, ad targeting or fraud detection. Black-box algorithms erode accountability.
Uneven economic impacts
While enhancing customer experience through hyper-personalization, AI runs the risk of disadvantaging those without access or understanding of new technological changes. Customers lacking internet connectivity, digital literacy or control over personal data may find themselves faced with less opportunities or higher prices.
For example, one study found lower income groups were more likely to see higher online prices driven by AI-based price optimization algorithms. [4] Companies have a role to play in addressing these potential digital and economic divides, whether through low-cost access programs, user education, alternative service channels or non-targeted options for all. The benefits of AI-driven personalization should aim to be as inclusive as possible.
In conclusion, organizations have an ethical duty to thoughtfully consider issues like privacy, bias, transparency and fairness when applying customer data and AI technologies. Building trust requires responsible governance of algorithms from the start. With open dialogue and proactive safeguards, the customer experience enhancements of AI can be realized while also respecting individuals and promoting equity for all.
