Challenge

Imagine Jimmy, a student in Trentino in 2084. His Mind Reading Hat enables him to reply to messages, monitor cognitive activity, and practice mindfulness exercises.

Would you buy such a Mind Reading Hat? Similar devices already exist in today's market.

Images: Muse Headband, Emotiv EPOC X, NeuroSky MindWave.

Current examples include the Muse Headband, Emotiv EPOC X, and NeuroSky MindWave—wearable, non-invasive brain-monitoring tools marketed to consumers. These devices promise insights into mental states, cognitive performance, and emotional well-being, positioning themselves as 'Fitbits for the mind.'

But are they truly comparable to fitness trackers? What distinguishes them from other wearable technology?

The Foundation: Understanding EEG Technology

Most consumer devices utilize Electroencephalography (EEG), which captures real-time brain activity by sensing magnetic fields on the scalp. When neurons fire, they produce electric charges, with different brain areas activating for different purposes. This forms the foundation of mind reading: by identifying active brain regions, the technology can infer what's being processed (e.g., motor movement versus verbal thought).

Image credit: hopkinsmedicine.org

Findings

Three critical distinctions from traditional wearables.

1. Brain Data Interpretation

While fitness trackers have familiarized us with straightforward body metrics (steps, heart rate, sleep patterns), brain data requires complex interpretation to derive meaning. There's no direct translation from raw brain signals to mental states like "happy" or "relaxed." Instead, the technology must approximate these states by matching brain activity patterns to predefined categories.

This interpretation process introduces significant bias—the device isn't truly reading happiness, but rather identifying activity patterns that align with its predetermined definition of happiness. Unlike checking if a fitness tracker accurately counts steps, users have no way to verify the accuracy of brain data interpretation.

2. Technological Mediation of Mental States

When devices interpret brain activity, they create a technological mediation between individuals and their minds—similar to how a thermometer represents temperature numerically or an ATM mediates banking interactions.

However, when this mediation involves our minds, several risks emerge:

• Altered Self-Perception If an app categorizes brain activity into five states (relaxed, creative, focused, excited, stressed), users might begin viewing their cognitive capacity through this limited lens. This reductionist perspective diminishes the true complexity of human cognition.

• Context-Dependent Interpretation Without proper context, similar brain patterns might be misinterpreted. For instance, brain activity indicating stress during danger could be identical to excitement before a wedding. Context is crucial for accurate interpretation.

• Psychological Impact Continuous monitoring could create negative feedback loops—being repeatedly told you're stressed might induce stressed behavior. Additionally, how might users react to concerning readings, such as indicators of a mental breakdown?

• Responsibility Displacement During rehabilitation, patients might overly rely on technology rather than their personal judgment—"I don't feel well, but my device says I'm fine, so I won't worry." This could prevent necessary intervention.

3. Privacy, Autonomy, and Agency

We've grown accustomed to digital monitoring of our online behavior, understanding how our browsing history and social media activity shape our digital experience. In professional settings, data monitoring can influence workplace relationships.

However, the mere awareness of brain monitoring can fundamentally alter thought processes. Unlike traditional digital monitoring, mind reading technology crosses the boundary between internal mental life and external observation. As Rainey et al. (2020) emphasize,

"Where mental privacy is uncertain, it is not clear that someone may feel free to think their own thoughts."

Privacy of thought is fundamental to decision-making, providing space for autonomy and agency. As these technologies become mainstream, we must carefully consider their impact on our deliberative processes.

Design Guidelines for Responsible Innovation

The unique challenges posed by these technologies demand a proactive approach to ensure future developments align with societal values and individual rights. Recent literature suggests three key themes for responsible innovation:

1. Anticipatory Approach

Instead of addressing ethical implications retrospectively, they should be considered during the earliest stages of research and development. This prevents a 'delay fallacy' where major issues surface only after market release.

Risnes et al. (2024) advocate using speculative design scenarios in workshops and focus groups to explore value dilemmas. Their case study identified three primary categories:

• Healthcare and Responsibility: Balancing technological opportunities with reductionist healthcare perspectives

• Self-care and Autonomy: Examining individual rights and responsibilities in health decisions

• Justice and Society: Addressing transparency, privacy, and potential data misuse

Image: An illustration of two visions: ‘the digital physiotherapist’ and ‘brain-controlled prosthetics’ (Risnes et al., 2023)

2. Embedded and Adversarial Ethics

Andorno and Lavazza (2023) propose two complementary models for safeguarding mental privacy and ethical development:

Embedded Ethics This model integrates safeguards into design and production through scientists, designers, and developers' initiative. As Rainey et al. (2020) note, "User control over neurotechnologies would appear to be of great importance in mitigating the potential mind-reading risks to privacy, autonomy, agency, and self representation." Key features include:

• Fine-grained data collection and interpretation control

• Data retraction and deletion capabilities

• Clear explanations of technology function and data meaning

Adversarial Ethics This approach relies on external parties—lawmakers and civil society—to enforce ethical and legal standards. Midha et al. (2022) observe that "Currently, however, no mandatory governance framework specifically for brain data has been established in supranational or international law." Potential measures include:

• UNESCO International Bioethics Committee (2021) proposed "neuro-rights"

• Guidelines for marketing consumer neurotechnology devices (Wexler and Thibault (2019) advocate that "companies marketing consumer EEG devices substantiate their claims with adequate evidence")

• Consumer-grade device data validity standards

• Restrictions on brain data use in sensitive contexts

3. Interdisciplinary Collaboration

Risnes et al. (2023) emphasize that responsible innovation requires a holistic approach considering technical capabilities alongside broader implications. This perspective encompasses:

• User experience and perspective

• Context of use (healthcare, workplace, personal)

• Potential societal impacts

• Long-term effects on cognition and self-perception

Livanis et al. (2024) advocate for multidisciplinary collaboration across neuroscience, engineering, ethics, law, and psychology. Andorno and Lavazza (2023) further emphasize involving scientists, lawmakers, and civil society in developing ethical standards.

Conclusion

Designers serve as natural bridges between engineers and policymakers in technological development. This position carries profound responsibility, requiring careful consideration of ethical, social, and human-centric aspects to ensure new technologies align with societal values and individual rights.

A complete version of this research, including full references, is available below.