The ethics of AI in market research: Why responsible data is a brand asset

Can AI help you understand your audience without losing their trust? Pureprofile CEO Martin Filz explores why ethical data use is fast becoming a brand asset, not just a compliance box.

Hyper-personalisation is the leading focus for brands and marketers, but it comes at the risk of privacy issues. For marketers, AI-driven market research offers huge potential, but must walk a fine ethical line. The organisations that get it right will win consumer trust.

AI is seeing unprecedented adoption across all spheres of life and work. Nearly 13 million Australians are actively using AI with usage time up 400% year-over-year. Clients now expect immediate access to insights with no delays.

AI is also reshaping research in profound and powerful ways. It is enabling predictive analytics, real-time sentiment and automated profiling. AI tools can automatically generate surveys and translate them across multiple languages and perform preliminary data processing and pattern recognition. They can even generate synthetic data – realistic but artificial responses – allowing brands to test products, messages or scenarios with 'virtual consumers'.

New powers bring new risks

One of the most significant concerns is the "garbage in, garbage out" principle. Poor foundational data can contaminate AI systems, creating flaws with major implications, particularly in areas such as synthetic data. If original datasets are biased or limited, synthetic data can amplify these flaws. The quality depends heavily on the original data source and provenance.

A major and growing threat to data integrity is the dominance of non-human traffic online. Today, it's estimated that up to 50% of internet activity comes from bots. While some bots are harmless or even helpful, many are malicious or misleading. This can pollute datasets, leading AI systems to draw the wrong conclusions about human preferences or behaviour.

This erosion of data quality is closely tied to a broader issue: declining consumer trust. In an AI-driven advertising ecosystem already flooded with scams, deepfakes and impersonations. This poses a particular challenge for newer or lesser-known brands, which often struggle to establish credibility and differentiate themselves from bad actors.

As a result, data security is becoming a competitive differentiator. Poor practices will damage brand equity, but businesses can increasingly build brand loyalty through transparent data practices. Emerging norms include zero-party data, value exchange models and the rise of "privacy by design".

Creating transparency standards

Rather than wait for regulation, marketers should get ahead of the curve and take a more proactive approach to transparency. This means creating guidelines, standards and transparency tools. This will not only lead to greater consumer confidence but also a higher chance of sensible regulation.

Organisations should always consider going beyond basic compliance. Consumer profiling for example is an ethical grey area. Just because you can predict behaviour, should you always act on that? There’s a need for empathy and restraint with data use.

In an environment where it's easy to create fake brands and misleading content, authentic brands that maintain transparent practices and build genuine trust will have significant competitive advantages in maintaining customer loyalty and brand equity.