Introduction – The AI and the invisible side of our digital lives
In the heart of the digital age, artificial intelligence (AI) it has crept into every corner of our online experience. From the tips of purchase to the anti-spam filters, by chatbot systems of monitoring of the network, the AI has become thethe architect is invisible that shapes what we see, we read and what we do.
This continuous interweaving between IA and everyday life raises, however, crucial questions on our privacy digital. Who is collecting the data? Why? What is the guarantee?
In this article we explore the challenges and ethical dilemmas that emerge from the encounter between the unstoppable progress of AI and our the fundamental right to privacy. On a journey to understand how to find your way in a landscape that is complex and evolving.
IA and data collection: in an era of always-on connectivity
The engine of artificial intelligence consists of a resource precise: the data. In our time, every click, message, search, and interaction with digital powers this system. We are immersed in a continuous connectivity, often unconscious.
But as is the case with this collection? The methods are varied – and often invisible. Cookies track our browsing habits; the social media map tastes, relationships, and interests; devices in IoT, such as smart speakers, smartwatch, monitor, position, health, routine.
These data can be classified into categories:
- Location: where we are and move
- Preferenceswhat we watch, buy, comment
- Communications: email, messages, interactions digital
This is not collected and isolated, but a systematic flowoften centralized in a giant database. If, on the one hand, this enhances the efficiency of the systems of IA, on the other, increases the risk of privacy violations, use, misuse, or invisible surveillance.
We are thus faced with one of the great ethical challenges of our time:
how to reconcile the innovation of AI with the protection of the private sphere of each of us?
Key Technologies: Profiling, Surveillance and Recognition
Artificial intelligence has introduced practices as powerful as it was controversial. Among these, three technologies in particular have a direct impact on our privacy digital: profiling, automated surveillance and the recognition of emotions.
Profiling: the digital portrait of each one of us
The profiling is like a magnifying glass algorithmic. Analyzes the traces that we leave online — purchase history, drug social, sites visited, searches, movement — to build a “profile predictive” our habits, tastes, and even vulnerability.
It is widely used in the personalized advertisingin credit scoring and even in the processes of selection of personnel, where automated systems analyse your CV and online behaviour.
But this technology, if not regulated, it has three main risks:
- Discrimination: the data reflect the bias of the company and them amplify;
- Manipulation: profiles can be used to influence opinions and decisions;
- Restriction of the choices: the “custom bubble” can limit ourselves, showing us only what confirms our tastes.
Depth related: The IA Unjust: how the algorithms inherit our biases
Automated surveillance: the digital eye that is watching us
If profiling is a lens, the automated surveillance is an eye always open. The AI is able to collect and analyze real-time data from cameras, microphones, smartphones, and sensors for monitor behaviors, movements, and interactions.
Technologies used:
- Facial recognitionused in the contexts of public and private;
- Behavioral analysisto identify “anomalies” in the movements;
- GPS tracking, active in several apps and mobile devices.
These solutions are adopted to urban surveillance, control employees, airport security. But the risks are serious:
- Effect of chilling: the sense of being stared reduces freedom and spontaneity;
- Abuse of power: it can become an instrument of control, matt;
- System errors: the false positives can have serious consequences.
See also: Surveillance and Artificial Intelligence: Who is controlling who?
Recognition of emotions: read the invisible
Some of the applications of AI are trying not only to observe what we do, but to understand how we feel. The the recognition of emotions analyze physiological signals and behaviour to infer the emotional states of a person.
The data analyzed:
- Facial expressions
- The tone and pace of voice
- Posture
- Signals biometric (heartbeat, skin conductance)
- Written texts
Fields of application:
- Emotional Marketing- to analyze reactions to products/advertisements
- Human resources: to evaluate soft skills in the talks
- Education and training: monitoring of stress and care
- Security: identify “suspicious behaviour” in airports or events
But even this technology is full of pitfalls:
- Poor reliability of scientific: emotions = mixed signals
- High risk of error: a false positive or false readings
- Violation of the private sphere: to examine emotions without consent invasive
- Manipulation: those who “read” the emotions may also want to check
External source of profit: THE Now Institute
However, the recognition of emotions is a technology that raises major ethical questions related to The ethical risks of the recognition of emotions
In spite of its promises, the recognition of the emotions, raises series ethical concernsrelated to the four basic points:
- Fragility scientificthe correlation between physiological signals and emotions is neither universal nor stable. The emotional states of human are complex, influenced by individual factors, cultural, and contextual.
- Inaccuracy and risk of error: these systems can generate false positives or false negativeclassifying incorrectly expressions, or intentions, with potentially serious consequences in the fields of employment, education, or safety.
- Invisible manipulation: if used without consent, the systems and the emotional are likely to influence the behavior of people in a subtle manner, driving consumption choices, opinions, or mood.
- Violation of the private sphereemotions are part of our intimacy. To detect them, analyze them or store them without transparency undermines the freedom, emotional and relational of individuals.
👉 In summary, the recognition of emotions is a the technological frontier high-risk. For this, they serve clear rules, collective awareness and an ethical approach to rigorous able to balance innovation and the protection of fundamental rights.
The Framework of Ethics and Law: Rules, Principles and Protections
Face the challenges of AI in the field of privacy requires not only technical skills, but also a solid compass ethics and an up to date knowledge of the reference standards. We can not allow that innovation to proceed without rules, putting at risk the fundamental rights of the people.
The GDPR and the principle of protection
At a global and regional level have been introduced laws to protect the personal data and promote the responsible use of artificial intelligence. In Europe, the The General regulation on Data Protection (GDPR) represents the pillar of a regulatory reference.
The GDPR sets out the key principles that should guide any processing of personal data:
- Lawfulness, fairness and transparency: the data must be collected in a lawful manner and treated with clarity, informing always the interested party.
- Limitation of purposesthe data can be used only for specific purposes and legitimate, declared in advance.
- Data minimisation: you must collect only the bare minimum, avoiding collected excessive.
- Accuracy: the data must be updated and corrected when necessary.
- Limitation of the conservation: the data shall not be kept for longer than necessary.
- Integrity and confidentialityit is essential to ensure the safety against unauthorized access and loss.
- Accountability (accountability): who collects and manages the data must demonstrate at all times to respect these principles.
These criteria are the basis of the legal minimum, but alone are not enough. In an era of artificial intelligence is widespread, it is necessary to rethink data protection in key algorithmic, where the automatic decisions can have profound impacts, and invisible.
The Framework of Ethics and Law: Rules, Principles and Protections
Face the challenges of AI in the field of privacy requires not only technical skills, but also a solid compass ethics and an up to date knowledge of the reference standards. We can not allow that innovation to proceed without rules, putting at risk the fundamental rights of the people.
The GDPR and the principle of protection
At a global and regional level have been introduced laws to protect the personal data and promote the responsible use of artificial intelligence. In Europe, the The General regulation on Data Protection (GDPR) represents the pillar of a regulatory reference.
The GDPR sets out the key principles that should guide any processing of personal data:
- Lawfulness, fairness and transparency: the data must be collected in a lawful manner and treated with clarity, informing always the interested party.
- Limitation of purposesthe data can be used only for specific purposes and legitimate, declared in advance.
- Data minimisation: you must collect only the bare minimum, avoiding collected excessive.
- Accuracy: the data must be updated and corrected when necessary.
- Limitation of the conservation: the data shall not be kept for longer than necessary.
- Integrity and confidentialityit is essential to ensure the safety against unauthorized access and loss.
- Accountability (accountability): who collects and manages the data must demonstrate at all times to respect these principles.
These criteria are the basis of the legal minimum, but alone are not enough. In an era of artificial intelligence is widespread, it is necessary to rethink data protection in key algorithmic, where the automatic decisions can have profound impacts, and invisible.
The official source for more information: EDPS – european data protection data
Ethics and innovation: beyond the rules, to the shared responsibility
The rules are basic, but from the sun not enough. To ensure the responsible use of artificial intelligence is also needed shared ethical principlescapable of driving the technological choices and public policies.
Here's the pillars of an ethical approach to data management in the era of AI:
- Consent: every individual must be able to decide if and how their data are collected, processed and used.
- Transparency: the mode of operation of the IA should be understandable, accessible, and understandable.
- Accountabilityorganizations should be responsible for the decisions taken by their algorithms, with the possibility of control and external audit.
- Non-discriminationthe IA must not generate bias or reproduce social inequalities, cultural or economic.
Technologies that protect the privacy
In addition to the principles, there are technical solutions that allow you to combine artificial intelligence and confidentiality:
- PET (Privacy Enhancing Technologies): tools that protect data during processing, minimizing the risk of exposure.
- Federated Learning: a technique that allows to train models IA without centralize the data, leaving them where they are generated (e.g. on user's devices).
These approaches are not yet the norm, but they represent the future of AI more respectful, decentralized and transparent.
A challenge that concerns us all
Build a digital ecosystem where IA and privacy can co-exist it is one of the biggest challenges — and more important — of our time. It is not enough to delegate: you need a shared commitment between policy, enterprises, developers, academics, and citizens.
Only with a governance and collective design manager will be possible to design a future in which innovation does help the person, and not reduce it to a variable to be optimized.develop innovative technological solutions that put at the center of the rights and freedoms of the people.
Case study: where AI meets (and challenge) the privacy
To understand the real impact of artificial intelligence on privacy, it is useful to pass from theory to practice. Below are three examples that show how the technologies and AI are intertwined, and it is often problematic, with our digital rights.
1. Facial recognition and public surveillance: the case of Clearview AI
More and more police departments adopt technologies of facial recognition to identify suspects through surveillance images. But these applications are not without risks.
An emblematic case is that of Clearview AIthat has created an enormous database of faces collected from all over the web, powering a system of recognition of power ever seen. This has raised international concerns on the monitoring of the mass and has led to sanctions by the european authorities for violation of privacy regulations.
The point is: how to balance public safety with the protection of individual freedom?
2. Advertising, predictive, and personalized feeds: when the algorithms we read
Algorithms profiling to analyse our every action online — purchases, like, and navigation — to show us advertisements on the measure. This mechanism is at the basis of the business model of many platforms, but it raises ethical questions relevant.
- The content displayed in the feeds of social are not neutral: they are the result of automatic selection.
- Users often do not know how and why see some back
- The risk is that invisible manipulation the opinions and behaviors.
For this reason, the GDPR requires explicit consent for the profiling of advertising and the use of data for marketing purposes.
3. Wearable devices and health data: health or control?
Smartwatch and wearable gather enormous amounts of data on our state of health: beats, sleep, and movement. The artificial intelligence processes these data in order to offer early diagnosis, monitoring, personalized and predictive medicine.
But what happens if this data falls into the wrong hands?
- An employer may monitor the performance biometric employee.
- Insurance may increase the premiums who has a “risk profile” is not in compliance.
- You may transform care in control, and the prevention of exclusion.
In addition to the individual cases: towards a culture of responsible production
These examples clearly show that the IA it is not abstractbut deeply influences on daily life. Privacy cannot be addressed only at the rear.
Need solutions proactive:
- Integrate data protection by design (privacy by design)
- Define mechanisms clear of responsibility
- Promote a informed public debate
- Increase the awareness of the users
Only in this way will we be able to shape a digital future in which IA and privacy can really live — and do it in the right way, the human and transparent.