AI
Responsible data management: How to remain ethical when implementing AI solutions
New tech carries risk, and while retailers should be encouraged to integrate AI across sectors, ethical data management must remain central to any decision.
February 18, 2025 by Adam Herbert — CEO, Go Live Data
In 2018, it was revealed that Amazon developed an AI-powered hiring tool to automate candidate screening. However, the tool was found to be biased against women because it had been trained on 10 years of hiring data that predominantly included male applicants. As a result, the project was eventually abandoned after it became clear the tool could not be corrected to eliminate bias. Amazon faced criticism for perpetuating gender inequality through its AI systems.
For retailers venturing into AI, this cautionary tale underscores the critical importance of ethical data practices. AI can be transformative, but companies must recognize their responsibility in mitigating risks to privacy, equity, and trust. The real challenge isn't just creating effective AI but ensuring that the journey from data collection to AI deployment is ethical at every step.
Why it matters
Data is the backbone of AI. Without it, AI systems wouldn't function. However, data is more than numbers, it represents people, behaviors, and lives. We know that as CSR and legal frameworks have been implemented the majority of companies are taking privacy seriously, and some even go beyond the legal requirements to protect customers. Nevertheless, it's good to be reminded about the dangers of infringing privacy and why neglecting ethical practices can be detrimental to business relationships.
Quality over quantity
Ethical AI starts with how companies collect data. Organizations should prioritize transparency, telling users that their data is being collected and why. Consent must be easy for the buyer to see and genuine, not buried in pages of fine print. Instead of vague statements like "We collect your data to improve services", companies could specify, "We analyze your usage patterns to enhance our recommendation algorithms, which help provide personalized experiences."
What's also important to note is data minimization should be the goal. For example, an e-commerce platform doesn't need to store a user's precise location when general regional data is enough for marketing purposes. Collecting excess information increases risks without adding value.
Finally, businesses must be vigilant about how they acquire third-party datasets. Buying or licensing data from external sources is common, but companies should scrutinize these datasets to ensure they are obtained ethically. Misusing unethically sourced data — such as scraping user profiles without consent—can lead to reputational harm and legal liability.
Bias prevention and fairness
As humans, we are at risk of being biased, and AI is no different. AI models learn from the data they are fed, and if that data contains historical inequalities, the AI will perpetuate them. Companies must actively counteract these biases rather than passively accepting them as "the nature of the data."
Look at it this way. If AI is meant to serve diverse populations, the data must reflect those populations. An AI healthcare tool trained primarily on data from white, male patients may fail to diagnose conditions accurately in women or people of color. Regular audits of data and AI models can also help uncover hidden biases before they become problematic.
Protecting privacy and security
AI relies on massive datasets, many of which contain sensitive personal information. Companies must ensure that this data is anonymized or pseudonymized to protect individuals. Even anonymized data, however, can sometimes be re-identified through advanced techniques, so robust safeguards are critical. This can involve implementing strong encryption, access controls, and regular security audits to help prevent unauthorized access.
A relatively new consideration is data ownership. Some companies are exploring ways to let users retain control of their data while still benefiting from AI-driven insights. For example, decentralized data models or blockchain-based systems could allow individuals to "lend" their data to AI systems without giving up ownership permanently. This approach not only bolsters privacy but also aligns with emerging legal requirements.
Transparency and accountability
Users should know how decisions are made, especially when AI affects critical aspects of their lives, such as loan approvals or medical diagnoses. Companies can achieve this by explaining AI processes in plain language and providing accessible documentation. While full technical details might overwhelm the average person, offering clear, understandable summaries can bridge the gap.
Accountability is equally important. If an AI system makes a harmful decision, who is responsible? As a leader, you must establish clear governance and take issue when malpractice occurs. This includes assigning responsibility for outcomes, whether through internal AI ethics boards or external oversight bodies. Openly admitting mistakes and taking corrective action also go a long way in maintaining public trust.
Long-term commitment to ethical practices
Ethical data management isn't a one-time effort; it's a continuous commitment. Regularly revisiting policies, conducting independent audits, and seeking stakeholder input ensure that companies stay on course.
Companies should view ethics as a business advantage rather than a burden. Consumers are increasingly favoring brands that align with their values. By championing ethical AI, businesses can differentiate themselves in competitive markets while contributing to societal good.
New technology carries risk, and while businesses should be encouraged to integrate AI across sectors, ethical data management must remain central to any decision. Leaders don't have to go it alone; they can reach out for help from outside sources should they need support. Those who prioritize responsible data management not only avoid pitfalls but also build stronger, more sustainable relationships with their customers. The road to ethical AI is challenging, but it is one that forward-thinking companies can and must navigate successfully.
About Adam Herbert
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