The Shadow of the Injury
In a world increasingly permeated by the artificial intelligence (AI), in which the algorithms filter the information that we consume, they guide the decisions we make and shape the opportunities that we have, you stand out a challenge to the dark and insidious: the bias algorithmic. These preconceptions, like a shadow, junk projected from the past, are transmitted from humans to thinking machines, feeding systems, IA and distorting their work. Thus, the automated decisions, that promise rationality and objectivity, are likely to play, if not amplify, the injustices and inequalities that plague human society. But where do these biases? What are the concrete consequences and, often, hidden behind their actions? And, most importantly, how can we build an AI truly equitable and inclusive growth, which becomes the engine of progress rather than the amplifier of injustice? To these questions we will attempt to provide comprehensive answers in this article, thoroughly exploring the intricate dynamics of the bias algorithmic strategies to present them and the best practices to mitigate the impact.
The raw Material Flawed: The Origins of Bias in Algorithmic
To fully understand the complexity of the problem, it is fundamental to start from the very foundations of artificial intelligence systems: the data. The algorithms, in fact, are not entity-thinking which are autonomous, but apprentices diligent that learn by observing and analyzing the data that is supplied to them, by identifying patterns, correlations, and statistical reports. In other words, we can compare the systems of IA students who assimilate with precision what is taught, with the risk, however, internalize the errors and distortions inherent in the instructional material.
The data of the training, in this sense, they become the prism through which the AI system interprets reality. If this prism is distorted, if it contains prejudices historical, cultural stereotypes, partial representations or untrue of the world, the IA will, unwittingly, they will absorb the distortion, reproducing them and amplificandole in his decisions and his actions.
This “raw material” can take on different forms, each with their own specific risks of introducing distortions. The historical datafor example , inevitably reflect the inequalities and the discrimination of the past. If an AI system for the selection of the personnel is trained on data that document a clear predominance of men in certain roles in the company, you learn how to promote automatically all the male candidates, perpetuating a gender discrimination that it was hoped to overcome. The sample datathey , in turn, may introduce bias if not accurately represent the diversity of real-world population. A facial recognition system trained mostly on images of people with light skin, for example, it may be less accurate and effective in identifying people with darker skin, with potentially serious consequences in the field of security and surveillance. Finally, even the data generated by usersapparently “neutral” and “objective”, can be spoiled by the prejudices latent in the language and behavior of individuals online, as in the case of algorithms of content moderation that can filter or censor unintentionally certain points of view, based on key words or expressions associated with certain ideologies or social groups.
Perverse effects: Discrimination in the Digital age
The consequences of the bias algorithms are anything but abstract or theoretical; on the contrary, they manifest in very concrete and tangible in the daily life of the people. Discrimination algorithmic is a reality more and more present, that creeps in different sectors and services, eroding the equity and undermining confidence in the institutions and technologies that more and more we use.
Consider, for example, automated systems for the selection of staff that filter the curricula vitae of the candidates on the basis of criteria that are questionable, penalizing unfairly, and people from certain cultural backgrounds or educational. Consider, also, the credit scoring algorithm, which analyzes the financial data, and behavioral of people to determine their creditworthiness, with the risk of disadvantaging those who live in low-income neighbourhoods or belong to ethnic minorities. In the same way, the systems, the prediction of recurrence used in the field of justice may finish to overestimate the risk of crime in some communities, leading to judgments more severe and imprisonment unjustified. Even the health is not immune from this danger: software medical assistance can provide treatments less effective in certain groups of patients, on the basis of data that reflect inequalities in socio-economic or health history. And even social media, which should be places of connection and sharing, can be transformed into resonators to the bias algorithmic, with algorithms that amplify unintentionally hate speech or content, polarized, thus contributing to poison the social climate and to divide the community.
In all these examples, the negative consequences of the bias algorithms are obvious: the perpetuation of historical injustices and social prejudices, the rise of economic inequality and social, the limitation of the opportunities of the individual, the erosion of trust in institutions and decision-making processes automated.
Technical smascheratrici: Surveys to test the algorithm
Fortunately, the human brain is not powerless in the face of potential narrowness of the bias hidden in the algorithms. In fact, there are different techniques and methodologies that can be used to identify and expose these distortions, with the goal of making systems AI more transparent, impartial and accountable.
The metrics of fairnessfor example , the statistical and mathematical tools that allow you to measure whether or not an algorithm is fairly different groups of people. You can define specific metrics for the different application contexts, verifying, for example, if the success rate of an intake is the same for male and female candidates, or if the risk of recurrence is estimated from an algorithm of police predictive is the same for people of different ethnicities.
The audit algorithmicin their turn, represent a sort of “inspection” the systematic and in-depth systems of IA. During an audit, you carefully examine the data of training used, the internal workings of the algorithm (when you can access it) and its decisions, trying to identify any patterns or trends that may suggest the presence of bias. It is a complex process that requires multidisciplinary skills and a deep understanding of the context in which the AI system is to be used.
Finally, the sensitivity tests consist in the change in a controlled way, the incoming data to the AI system, to see if it reacts in an unexpected way or discriminatory. For example, you can create slightly different versions of the profiles of the candidates for a job, changing only the name, or address, to check if the system evaluates them differently on the basis of these characteristics.
These techniques smascheratrici, applied with rigor, creativity, and awareness of the limits of each, proved very valuable to shed light on the inner workings of the algorithms and to make the systems IA more responsible and reliable.
Corrections and Adjustments: The Protocol of the Anti-Bias
Once identified, the bias algorithmic, the next challenge is to correct and mitigate the impact. To this end, it can adopt a variety of strategies that act on different levels of the process of development and implementation of AI, each with their own strengths and their limitations.
The pre-processing of the data pointing to “clean up” the raw material, that is, the data of training, removing, correcting or balancing the data to reduce the risk of introducing bias. You can use the techniques of stratified sampling, over-sampling or under-sampling to ensure that all groups are represented adequately in the dataset.
The modification of the algorithmsinstead, it provides for the adaptation of the equations and the mathematical models that form the heart of the AI system, to reduce or eliminate the bias that may be inherent to their structure. Some algorithms, for example, are designed to enhance “fairness”, and the accuracy in the joint, trying to find a compromise between the two objectives.
Finally, the post-processing of the results intervenes directly on the predictions of the AI system, making adjustments and corrections to ensure that the final decisions are fairer. For example, you can define different thresholds for the approval of a loan or for the allocation of a resource, depending on the group membership of the applicant.
It is important to stress that there is no one single solution, and finally to the problem of bias algorithmic. The mitigation strategy more effective will depend on the specific context in which the AI system is used and the nature of the data and the algorithms involved. Often, the best solution is to combine different techniques, adopting a flexible approach and an iterative procedure, in which the AI system is continuously monitored and corrected in time.
A multi-disciplinary Challenge: Beyond the Algorithm
The fight against the bias algorithmic cannot be reduced to a simple technical question, the exclusive preserve of specialists in computer science. On the contrary, is a challenge that is complex and multidimensional, which requires an interdisciplinary approach and collaboration between experts from a variety of disciplines.
The philosophers they can provide valuable contributions to define the concepts of fairness, justice and impartiality, helping us to identify the ethical roots of bias algorithmic and to formulate guiding principles for their prevention. The sociologists they can analyze social and cultural dynamics that contribute to the perpetuation of inequalities, providing useful insights for preventing the reproduction of these dynamics in the algorithms. The lawyersthey , in turn, can help us to interpret the existing laws (such as anti-discrimination laws) and to develop new legal instruments to regulate the use of AI and to protect the rights of the people. The communication experts you can use for this information, as difficult to understand, can be understood by a wide audience, so you can create a collaboration and active participation in the achievement of these new methods of regulation.
The transparency and l’accountability, which is the ability to render an account of their actions, are the two fundamental pillars of this interdisciplinary approach. The systems of IA should be understandable and explainable, so as to allow the control of the human, and the organizations responsible for the automated decisions. At the same time, it is essential to promote an open and constructive dialogue between all actors involved, from the developers of the IA to end-users, policy-makers at the common citizens, to build a digital future in which AI is a force in the service of equity and inclusion.
Build a Digital Future Right
Artificial intelligence promises to revolutionize profoundly our world, creating new opportunities and solutions to complex problems. But we can not afford to embrace uncritically this technology, ignoring the potential risks and ethical implications. The bias algorithmic represent a significant obstacle on the path towards a digital future that is more just and inclusive, a challenge that requires a joint effort, and the knowledge that our choices today will determine the type of company that will build tomorrow.
Addressing this challenge requires a coordinated effort and the knowledge that the solution is not merely technological. Must go through the development of algorithms managers, based on data that is accurate and unbiased, the implementation of control mechanisms, effective monitoring, and the creation of a regulatory framework, robust to protect the fundamental rights of people.
Only in this way we will be able to unlock the full positive potential of artificial intelligence, building a digital future where equity and inclusion are core values are not just empty promises.
