{"id":5752,"date":"2023-02-27T08:42:04","date_gmt":"2023-02-27T07:42:04","guid":{"rendered":"https:\/\/web.cidai.eu\/la-intel%c2%b7ligencia-artificial-la-mes-humana-de-les-intel%c2%b7ligencies\/"},"modified":"2025-12-01T17:51:09","modified_gmt":"2025-12-01T16:51:09","slug":"la-intel%c2%b7ligencia-artificial-la-mes-humana-de-les-intel%c2%b7ligencies","status":"publish","type":"post","link":"https:\/\/cidai.eu\/en\/la-intel%c2%b7ligencia-artificial-la-mes-humana-de-les-intel%c2%b7ligencies\/","title":{"rendered":"Artificial Intelligence, the most human of intelligences"},"content":{"rendered":"

Them Algorithms based on Artificial Intelligence (AI) have multiple applications<\/strong> and they are currently capable of revealing who likes whom, what music you love, how much water a crop needs, when a traffic light should turn red and how to modify prices in a business, among many other things that we do every day and that involve the intervention, often transparent, of artificial intelligence.<\/p>\n

TikTok’s proposal represents an important paradigm shift that the rest of social networks, such as Meta, will follow, and that moves us from a system of content recommendations based on our contacts, towards a system governed by an algorithm that proposes content based on a constant test<\/a>to maximize attention time.<\/p>\n

Currently, Artificial Intelligence can recommend at what temperature to program the combustion of waste according to its composition, what combination of molecules can save you from a fatal disease or how to anticipate who will die on the road during the Easter holidays (theadvertisement<\/a>of the DGT of 2022).<\/p>\n

TheCatalan Data Protection Authority<\/strong> published a complete report in 2020 reviewing cases of algorithms that make automated decisions in different contexts in Catalonia [ACPD 2020] and highlighted the existence of countless examples of use in areas such as health, justice, education, mobility, banking, commerce, work, cybersecurity, communication or society.<\/p>\n

In legal matters, Artificial Intelligence algorithms could complement or even replace (although they should not) a regulation that can be consulted in the Official Gazette and can be interpreted in different ways by various parties, or a decision-making body where its own decisions are in many cases appealable.<\/p>\n

In the field of law [Bahena 2012], beyond intelligent search engines for documents relevant to the resolution of legal cases, search engines that use AI to search for legal precedents based on keywords, there are numerous cases of intelligent systems that help in the preparation of lawsuits, responses, and even in the ruling of sentences and their subsequent argumentation under different forms of AI. These range from classic rule-based reasoning systems (such asGetAid<\/a>, from the Australian government to determine access to legal advice in criminal and family matters) [Zeleznikov 2022], to the most advanced hybrid architectures that combine, for example, automatic reasoning with artificial neural networks (such asSplit-up<\/a>which proposes to the Australian Court the division of property and custody of children in cases of separation and divorce) [Zeleznikov 2004].<\/p>\n

A recent example could be the request made by the Civio Foundation to have access to the source code of the BOSCO application<\/strong>, developed by the government and used by electricity companies to find out if a user in a vulnerable situation can receive the “social bonus”, that is, discounts on their energy bill. Despite having verified that many eligible applicants were not receiving this aid, the demand wasdenied<\/a>in the first instance against the Transparency Council report, claiming that it posed a danger to public safety (!). Civio has filed an appeal against this decision that leaves citizens unprotected against automated decisions that do not respect their rights.<\/strong><\/p>\n

It is important to keep in mind that The European Commission has taken decisive action to define a framework for the development of safe, ethical and trustworthy AI, and is in the process of drafting the European AI Act (AI Act) [EC AI act 2021]<\/strong>, where it is attempted to ensure that AI in Europe will be oriented towards the common good, will put the person at the center and will respect the fundamental rights of people, unlike the Chinese or North American vision, where, respectively, the control of the data is held by the government (surveillance and social credit systems) or by the company that owns the application that collects it (and monetizes it as it wishes).<\/p>\n

In fact, in 2018, in a pioneering way, the EC developed its ethical recommendations for a safe and trustworthy AI (TrustWorthy AI, TWAI) [CE ethics 2018] and the first of its axes was dedicated to Human Agency and Human Oversight in a clear attempt to prevent AI-based applications from being able to make decisions autonomously. In other words, the place that Europe proposes to reserve for AI-based applications is that of an intelligent assistant for the user, who will effectively make the decision, in such a way that there is always human validation of the recommendation, prediction or diagnosis proposed by the algorithm.<\/p>\n

On the other hand, in the Digital Rights Charter<\/strong> (Catalan [CDDcat 2019] and Spanish [CDDSpain 2021]) recognizes the right of people to be informed of the intervention of an algorithm in any decision that affects them, and the person also has the right to know with what criteria the algorithm has evaluated them and how the result of this evaluation has been prepared. This should allow the detection of possible biases in the operation of the algorithm that could increase social inequalities or limit people’s rights.<\/p>\n

Biases and explainability<\/h2>\n

Among many others, there are well-known, and quite scandalous, cases of gender discrimination in AI-based algorithms that evaluate funding requests in different channels. Without going any further, AppleCard, the credit card launched by Apple in 2019, offered up to 20 times more liquidity and a longer payment term to men than to women, even between spouses on equal terms [Telford 2019]. The granting of loans to entrepreneurship has also been the subject of scandalous comparative grievances for applications headed by women in many countries, such as Chile [Montoya 2020], Turkey [Brock 2021] or even Italy [Alesina 2013] and although the problem was detected as early as 2013, cases continue to occur in 2021.<\/p>\n

Preventing this type of situation directly impacts the type of algorithm that can be used to assist in decisions that affect people, because they must be algorithms capable of explaining or arguing why they make one recommendation and not another or why they make a certain prediction. There is actually a relatively new branch of AI that has gained a lot of traction and is known asExplainable AI<\/strong>(explainable AI), which deals with these issues. And at the moment, it is difficult to ensure that deep learning methods, which are making very good and very fast predictions about very complex realities, can adequately justify these predictions. This actually happens with all so-called black box algorithms, which include not only deep learning algorithms, but also all those based on artificial neural networks or evolutionary computing.<\/p>\n

The applicationChatGPT<\/a>Open AI opened to the general public on November 30, 2022 has an amazing ability to write all kinds of texts or lines of code, seemingly, on any topic asked of it<\/strong>; The barriers to the universalization of electric cars, the drafting of a standard rental contract or the code for an application that makes an avatar appear waving only when there is a human in front of the screen, are some of the topics on which it can provide answers.<\/p>\n

In all the tests carried out by the authors and many other users, the speed and plausibility of the answers offered by the application is surprising, although there are also deficiencies in content or relevant arguments in specific fields that make it difficult to consider the results offered as fully reliable.<\/p>\n

ChatGPT has been trained on millions of documents published up to 2021, but its creators have not revealed which documents these are. There are currently millions of people experimenting with the app right now. It would be interesting to know what the “universe” of knowledge used in training the tool was in order to understand the possible biases in the results it offers.<\/p>\n

Too promising to pass up<\/h2>\n

It is clear that the ability of algorithms to encompass complexity, to bring us closer to a desired objectivity, but above all their ability to generate economies of scale in knowledge-related activities constitutes too attractive an opportunity for progress to pass up, despite the risks already known, and others that we will undoubtedly discover in the coming years.<\/p>\n

Only through algorithmic organization can the Hong Kong subway most efficiently organize the ten thousand operators who every night carry out the 2,600 repair and maintenance tasks necessary to have a public transport service with very high levels of punctuality (99.9% since 2014!), and generate savings of 2 days of repairs per week and about $800,000 per year [Chun 2005] [Hodson 2014]. Since December 2020, the Barcelona metro has had a system based on Artificial Intelligence that allows it to control the capacity of platforms and trains, and open or close access to them to generate the safest conditions for passengers from the point of view of the spread of viruses [Nac 2020].<\/p>\n

When an algorithm is coded in a programming language understandable to machines, it becomes a tool that can scale its impact to a dimension unattainable by means of communication between humans. For example, updating the software of a fleet of connected vehicles or robots allows the improvements resulting from learning to be incorporated into each of them in a few minutes. In parallel, each update to the algorithms that manage our search or navigation tools opens and closes opportunities for businesses and citizens to discover each other.<\/p>\n

In reality, we are witnessing the emerging development of a very powerful technology, Artificial Intelligence, which like all new things generates certain fears and where good information can help dispel doubts. In this sense, on February 18, 2021, the government of Catalonia launched the Catalan Strategy for Artificial Intelligence [catalonia.ai 2021] to structure an action plan where the development and strengthening of the AI \u200b\u200bsector could become a driver of change, modernity and progress for the country and as part of the strategic axis of ethics and society, in May 2021 it launched an informative course on Artificial Intelligence aimed at providing basic training to citizens in general. The course, designed by the UPC research center, IDEAI (Intelligent Data Science and Artificial Intelligence research center), is free and can be accessed fromfrom this website<\/a>.<\/p>\n

Like all technologies, AI can present more or less ethical uses, with greater or lesser risk, and the challenge currently is to find that delimitation in the uses of AI that allows us to take advantage of all its benefits without being impacted by harm.<\/p>\n

If we go back in history, fire or the knife are technologies that, when they appear, radically change the course of humanity. Both, like AI and so many others, have two sides. Fire allowed us to warm ourselves and overcome frost, and also to cook, but if the necessary precautions are not taken it can cause burns and fires that can end in major natural disasters. The knife allowed new food manipulations and gave shape to new tools, contributing to the development of civilization, but it also serves to attack people, proof of this is that in all cultures we have developed rules that penalize undesirable uses of these technologies. However, despite these risks, no one would think of banning the manufacture and use of knives to protect us from their dangers and risks. And this is precisely what should also happen with Artificial Intelligence; Rather, it is about identifying the risks and regulating their uses to allow for beneficial development for all.<\/p>\n

Trust, the limitations of machines and the power of data<\/h2>\n

Yes Machine learning is the branch of AI that allows finding the best solution by applying the computational power, speed and learning capacity of machines to a mass of data.<\/strong>, it is then appropriate to evaluate those dimensions in which machines are reliable and the situations where the mass of data is adequate.<\/p>\n

Then the questions are: Who can we trust with our \u201cmass of data\u201d? Who can we entrust with the control of the machines so that they work at our service?<\/strong><\/p>\n

TheEdelman report 2022<\/a>points out that globally we are at the lowest point in trust in companies, NGOs, institutions and the media since they began this series in 2000. The succession of financial crises, institutional corruption, fake news, fake videos and COVID-19 have established distrust as the default sentiment towards institutions. The Dutch government was forced to resign on January 8, 2021, when it was shown that the AI-based SyRI system it had been using since 2014 to identify fraud in welfare recipients had a bias that only imputed immigrant families from vulnerable districts and had wrongly prosecuted 26,000 families, forcing them to return their benefits unfairly. Hundreds of families suffered this unjust institutional harassment, which led to depression, ruin, stigmatization, suicides, or imprisonment by mistake, because no one reviewed the algorithm’s recommendations with a critical spirit and context [Lazcoz 2022].<\/p>\n

In the digital sphere, Tim Berners Lee, creator of the www, criticizes how his own creation, initially intended to constitute the greatest tool for democratizing knowledge in history, has degenerated into an instrument ofdivision and inequality<\/a>through attention capture and behavior control and tries to develop a better alternative.<\/p>\n

In parallel, The development of new technologies has resulted in a concentration of algorithms and data in the hands of a few global supercorporations that increasingly influence more aspects of our lives, with the risks that this entails.<\/strong><\/p>\n

Ensuring that algorithms do not incorporate biases by design is one of the biggest challenges we face. In reality, algorithm design is based on the developer’s understanding of the real process that is being represented computationally and to acquire this understanding it is necessary to interact with the expert in this process and capture the relevant aspects to take into account in the implementation.<\/p>\n

In this transmission from the expert in the application domain to the computing specialist, implicit knowledge plays a very bad role. Neither is the expert in the domain aware that he has it, and that he uses it in his reasoning, decisions and actions, nor does he realize that he is not including it in his description of the world, nor is he transmitting it to the interlocutor; nor is the developer aware that he is making assumptions (often dangerously simplifying) that guide his implementation and that can bias the behavior of the algorithm. In addition, much of the implicit knowledge has a situated cultural component, that is, many social values \u200b\u200bor customs, and ways of doing things are valid in a society, organization or specific situation but are not universal. However, some criteria are activated opportunely in the human person in the face of certain more or less exceptional (or infrequent) situations, but they remain in the unconscious the rest of the time, and therefore, they cannot be passed on to the verbal, much less to the algorithm.<\/p>\n

Machines can apply computational power, speed and processing capacity but are not sensitive to context, unless they are given a good formal description of it; they are not capable of handling exceptions, if they have not been implemented to take them into account; nor can they deal with unforeseen events, unlike humans. This means that biased behaviors can appear in algorithms that deal with very complex phenomena.<\/p>\n

These biases are not always intentional. We often do not practice the analysis of the scenarios for which algorithms are built with sufficient precision. Defining criteria for the majority, for the general case, is almost always a bad idea, because it is in the exception, in the violation of the minority, where injustices appear.<\/p>\n

It is necessary to employ lateral thinking more thoroughly and perform a more complete analysis of possible exception scenarios to reduce bias in algorithm logic. Certainly, having diverse teams makes this task easier, since the combination of different perspectives on the same problem provides visions that complement each other and naturally reduce logical biases, but also biases in the data.<\/p>\n

In fact, most algorithms are powered by data, and we have lost the good habit of using the old theory of sampling and experimental design to guarantee that the data we use to train an AI will correctly represent the population, or phenomenon, under study and will not carry biases that corrupt the predictions and recommendations of the resulting systems.<\/p>\n

As Kai Fu Lee explains in his bookAI superpowers<\/a>the availability of data is more relevant than the quality of the algorithm.<\/strong> For example, algorithms for playing chess had existed since 1983, but it was not until 1997 that Deep Blue beat Kasparov, just six years after a database with 700 thousand games between masters was published in 1991. In a world where the core algorithms are public, it is the availability of data that determines the advantage.<\/p>\n

However, currently data is no longer just reduced to the numbers or measurable magnitudes that traditional statistics have been analyzing for so many years. Voice, images, videos, documents, tweets, opinions, our vital signs or the values \u200b\u200bof virtual currencies are today extremely valuable sources of information from which to extract relevant information in all directions, and constitute a new generation of complex data that is the object of intensive exploitation from the field of Artificial Intelligence. Video games, virtual simulations or the much-promised metaverse are built with data, and constitute computational metaphors of real or imaginary worlds. As Beau Cronin states, they may be preferable to the real world for those who don’t enjoy the “reality privilege<\/a>“: It is shocking, and deserves reflection, to note that today 50% of young people already consider that they have more vital opportunities in the online environment than in the real world.<\/p>\n

Decentralized or distributed data architectures, and emerging federated data science technologies are part of an intense debate on how to develop data policies, which are not only reduced to the supporting architectures where data is hosted, but also to how it is produced, data openness and tenure policies, ownership and business models (commercial or institutional?) and user licenses. Some argue that if the production of the data is distributed – Google’s “facelift” facial recognition algorithm is based on photographs of 82,000 people – its ownership should also be distributed. In some cases, the courts have also forced thedestruction of algorithms<\/a>generated through the deceptive obtaining of data, such as the Kurbo application that captured data from eight-year-old children without the knowledge of their parents or the “horrible app”, in MIT terms, that put women’s faces in pornographic videos from photos that could be taken openly from the internet (and which we do not link to the reference for prudence).<\/p>\n

Challenges and proposals<\/h2>\n

There is no doubt that human activity modifies living conditions on the planet and that it has done so at an accelerated rate over the last two centuries, to the point of making us aware of the climate and social emergency in which we live.<\/p>\n

Our collective challenge now is life: how to generate decent living conditions for the 8 billion people who inhabit the planet, and how to do it without our survival threatening the quantity, quality and diversity of life of other living beings or future generations.<\/p>\n

We cannot do without Artificial Intelligence as a tool to address the complexity, simultaneity and scale of these challenges, but as we have argued previously<\/strong>, nor can we let what is technically possible, even if not necessarily desirable, guide us in its development.<\/p>\n

AI is a technology created by humans and therefore incorporates many of the characteristics of our species, including the possibility of making mistakes; or having prejudices and preferences when training with biased data or its creators define criteria that are not equitable, or do not consider relevant cases, and this can contribute to increasing social inequalities or injustices. For this reason, it is essential to refine the methodologies for constructing training data, designing algorithms and validating them in order to guarantee solid and reliable AI, as well as developing a regulatory and legal framework that accompanies the sector, and citizens, with security.<\/p>\n

We believe that it is possible to formulate a new framework for the development of Artificial Intelligence at the service of life if we combine four interdependent areas of action: technological, methodological, legal and governance. We detail each of them below.<\/p>\n

Technological field: Decentralized technologies<\/h2>\n

If, as we have seen, the temptation of social or commercial control comes from the centralization of data, it is in our interest to promote technologies that, such as federated learning systems, allow us to extract knowledge from databases distributed in different locations, distributing the execution of the algorithms to be optimized to each of these locations and then extracting conclusions of general validity based on several iterations.<\/p>\n

Yes The massive deployment of the Internet of Things must help us manage the complexity and simultaneity of these challenges in each local context,<\/strong> We must also promote advances in edge computing capacity that enables autonomous response at all times without the need to transfer or process data in a centralized system. It serves as an example ofarticle<\/a>published in Nature on September 15, 2021, describing how an algorithm was generated that could predict with 92% reliability the oxygen needs of patients with Covid based on vital data and chest X-rays of patients from 20 hospitals without the need to centralize all the data in a single database.<\/p>\n

Blockchain can also contribute to accrediting and validating interactions between distributed agents for the generation of value, thus facilitating the development of complex systems oriented towards shared goals.<\/p>\n

Methodological scope: Hybrid knowledge-based\/data-driven methodologies with expert assistance<\/h2>\n

Since its birth, AI has gone through several stages. Initially, the focus was on expert knowledge and knowledge-based systems; Given its limitations, a paradigm shift towards machine learning took place where the focus has been placed on data and inductive learning processes.<\/p>\n

After almost forty years of machine learning and data-driven models, we realize that not everything can be represented through data, and the pure data-driven approach also presents limitations for the ambitious goals that are set with the use of AI.<\/p>\n

It seems that the most reasonable thing is to move to a third paradigm, that of Hybrid Artificial Intelligences – those that combine knowledge-based components with data in a cooperative manner – that preserve the benefits of both approaches and mutually mitigate their limitations<\/strong>; a paradigm where data reaches where the implicit knowledge of experts does not, and human knowledge provides the context that cannot be represented in the data.<\/p>\n

The third fundamental ingredient is the intense collaboration with the expert in the application of the AI-based system throughout the entire process of design, construction, choice of relevant data, data preparation, evaluation, validation and supervision of the system; especially in the contextualization and interpretation of results and in the supervision of the recommendations given by the system already in the production phase (human agency).<\/p>\n

The scheme that includes the “human in the loop” is the only one that guarantees that the data is relevant, that its use is justified and that what is done with it makes sense, which guarantees the correct contextualization of the results obtained. Co-creation schemes where potential users, in addition to AI experts and designers, are involved are more than recommended.<\/p>\n

Of course, it will be essential to have experts free from intentionality and in this sense it is interesting to add teams of specialists who provide the agreed knowledge in the field of application, leaving aside personal or tendentious views. This, however, has no more or less risk than the absolute need for the data that feeds the system to be of the highest quality, which falls into the hands of the same humans who intervene in the process.<\/p>\n

Legal scope: Legal and regulatory framework for data and algorithms<\/h2>\n

As has happened in other areas, The European Union is leading the development of a legal and regulatory framework that regulates the use of data and algorithms while respecting citizens’ rights to achieve an appropriate balance between the common good, economic competitiveness and social progress.<\/strong><\/p>\n

The general philosophy of this regulation, which states, regions and municipalities are developing in parallel, is to avoid scenarios in which data and algorithms are used inappropriately for commercial or social control of citizens.<\/p>\n

At the same time as mandatory legislative initiatives, various groups of citizens and professionals are also promoting initiatives that aim to guarantee appropriate use of data and controlled application of algorithms.<\/p>\n

For example, the citizen data cooperative for health researchSalus.coop<\/a>, designed the data use licenses alongside citizens through the TRIEM initiative, which presented several scenarios in which their data was requested and their acceptance or rejection was requested. The data is not simply \u201ccontent\u201d that can be shared under Creative Commons licenses and each scenario presented detailed (i) who requested it (ii) what the object of the research was (iii) how the research results would be shared and (iv) what risk of re-identification those sharing their data assumed.<\/p>\n

In parallel, groups of experts, professionals and governments are promoting the creation of seals and certifications, such as the ethical seal. PIO of the Artificial Intelligence Ethics Observatory of Catalonia (OEIAC) [PIO 2022]<\/strong>, an entity created under the auspices of the aforementioned Catalan AI strategy of Gencat, which accredit the explainability capacity of the algorithm, its recommendations and desired objectives or the representativeness of the data used in training with respect to the population affected by its application. On December 23, 2022, the Barcelona City Council put into effect the Protocol for the Definition of working methodologies and protocols for the implementation of algorithmic systems of the Government Commission, as part of the actions planned in the Government Measure of the Municipal Strategy for Algorithms and Data for the Ethical Impulse of Artificial Intelligence. This protocol, following European guidelines, establishes each step of the life cycle of an ICT service of the Barcelona City Council based on artificial intelligence, the studies, controls and strategies to be developed [IA-BCN 2022 Protocol].<\/p>\n

In the corporate sphere, a balance must be sought between the level of transparency and openness of the criteria used by an algorithm, and the company’s own trade secret or competitive advantage that guarantees its survival and development in a global market. In this context, it would seem socially necessary to clarify the decision criteria used, but it would be more difficult to open the code for examination.<\/p>\n

In the same sense, it would be necessary to establish policies for backrunning (reverse execution) of the algorithms on databases representative of the population they must affect in order to objectively validate the absence of bias and the social quality of the resulting recommendations.<\/p>\n

Finally, it is essential to inform citizens of those situations in which AI is making decisions relevant to their lives, as well as to promote the right to know the criteria used in decision-making and to resort to a qualified human interlocutor to address their complaints.<\/p>\n

The digital rights charters that are being drafted or are already applicable, developed in various areas, including Catalan or state, as we have already said, also formulate the right of citizens to refuse to have their data used for unwanted purposes, even if it is public data already in the possession of the administrations (opt out).<\/p>\n

Governance area: Participatory governance<\/h2>\n

The governance of data, the raw material that fuels Artificial Intelligence, is fundamental to the definition of its purposes. As we have observed, the corporate or institutional governance of centralized data masses can put at risk or limit fundamental citizen rights, or divert the potential of their use to purposes in conflict with the general interest or with socially relevant challenges at each time and society.<\/p>\n

In the face of citizens’ fears regarding the misuse of their data at an individual level, a set of new social institutions have emerged around the world that implement the collective governance of data for the common good, known as “data trusts”, literally data trusts.<\/p>\n

Data trusts currently adopt a multitude of legal forms; public, private or mixed foundations; data cooperatives or unions; or even DAOs (distributed autonomous organizations) based on a set of rules that operate on blockchain.<\/p>\n

In all of them the objective is to maximize the collective potential of data through participatory and transparent governance mechanisms that guarantee its use where citizens or the rules of the institutions that manage it on their behalf have provided their support directly and not for others.<\/p>\n

It is not the first time that humans have had the opportunity to replace our workforce with that performed by animals and then machines. We also know that this substitution is never complete or free of risks, especially in the initial stages, in which the fascination with new possibilities advances their adoption to social and regulatory changes to ensure that the ultimate result improves human life opportunities.<\/p>\n

It is clear that AI can help us successfully face the social and environmental threats that surround us today, because it currently represents one of our best allies in well-being and environmental protection. But for all these new possibilities, this new energy to be channeled in favor of life, we need to take the reins and develop a new ethical, technological and institutional framework that guides its development and implementation.[dt_fancy_separator][Bahena 2012] BAHENA, Goretty Carolina Mart\u00ednez. Artificial intelligence and its application in the field of law. Allegations, 2012, no. 82, p. 827-846. https:\/\/fuenteshumanisticas.azc.uam.mx\/index.php\/ra\/article\/view\/205<\/a> [ACPD 2020] Catalan Data Protection Authority: Artificial Intelligence: Automated decisions in Catalonia, ACPD, 2020 https:\/\/apdcat.gencat.cat\/web\/.content\/03-documentacio\/intelligence_artificial\/documents\/INFORME-INTELLIGENCIA-ARTIFICIAL-FINAL-WEB-OK.pdf<\/a> [Alesina 2013] ALESINA, Alberto F.; LOTTI, Francesca; MISTRULLI, Paolo Emilio. Do women pay more for credit? Evidence from Italy. Journal of the European Economic Association, 2013, vol. 11, no suppl_1, pp. 45-66.<\/p>\n

[Brock 2021] BROCK, J. Michelle; DE HAAS, Ralph. Discriminatory lending: Evidence from bankers in the lab. 2021.<\/p>\n

[catalonia.ai 2020] Catalan Artificial Intelligence Strategy, Generalitat de Catalunya 2020https:\/\/politiquesdigitals.gencat.cat\/web\/.content\/00-arbre\/economia\/catalonia-ai\/Estrategia_IA_Catalunya_VFinal_CAT.pdf<\/a> [CDDcat 2019] Catalan Charter of Digital Rights and Responsibilities. Government of Catalonia, December 2019https:\/\/politiquesdigitals.gencat.cat\/ca\/ciutadania\/drets-responsabilitats\/carta\/<\/a> [CDDSpain 2021] Charter of Digital Rights. Moncloa, July 2021 https:\/\/www.lamoncloa.gob.es\/presidente\/actividades\/Documents\/2021\/140721-Carta_Derechos_Digitales_RedEs.pdf [CE ethics 2018] High Level Expert Group on AI, EC (2018) Ethics Gruideiles for Trustworthy AI. EC. 2019.https:\/\/digital-strategy.ec.europa.eu\/en\/library\/ethics-guidelines-trustworthy-ai<\/a> [CE AI act 2021] The AI \u200b\u200bAct, EC, 2021https:\/\/eur-lex.europa.eu\/resource.html?uri=cellar:e0649735-a372-11eb-9585-01aa75ed71a1.0001.02\/DOC_1&format=PDF<\/a> [Chun 2005]Chun, HW, Yeung, WM, Lam, PS, Daniel Lai, Richard Keefe, Jerome Lam, Helena Chan, “Scheduling Engineering Works for the MTR Corporation in Hong Kong,” In Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence, Pittsburgh, July, 2005 [Hodson 2014]HODSON, Hal. The subway is run by AI. 2014.<\/p>\n

[Getaid xx] Getaid: Helping Victorians with their legal issues https:\/\/www.legalaid.vic.gov.au\/ [Lazcoz 2022] LAZCOZ MORATINOS, Guillermo; CASTILLO PARRILLA, Jos\u00e9 Antonio. Algorithmic profiling in the light of human rights and the General Data Protection Regulation: the SyRI case. 2022.<\/p>\n

[Montoya 2020] Montoya, AM, Parrado, E., Solis, A., & Undurraga, R. (2020). In bad taste: Gender discrimination in the consumer credit market (No. IDB-WP-1053). IDB Working Paper Series.<\/p>\n

[Nac 2020] Artificial intelligence to control the capacity in the Barcelona metro, El Nacional 2 December 2020 https:\/\/www.elnacional.cat\/es\/barcelona\/inteligencia-artificial-controlar-aforo-metro-barcelona-tmb_562131_102.html [PIO 2022] The PIO model (Principles, Indicators and Observables): A proposal for organizational self-evaluation on the ethical use of data and artificial intelligence systems https:\/\/www.udg.edu\/ca\/Portals\/57\/OContent_Docs\/modelpio-CAS-v5.pdf<\/a> [TELFORD 2019]TELFORD, Taylor. Apple Card algorithm sparks gender bias allegations against Goldman Sachs. The Washington Post, 2019.<\/p>\n

[Zeleznikov 2004] Zeleznikov, J. (2004). The Split-up project: induction, context and knowledge discovery in law. Law, Probability and Risk, 3(2), 147-168.<\/p>\n

[Zeleznikov 2022] Zeleznikov, J. An Australian Perspective on Research and Development Required for the Construction of Applied Legal Decision Support Systems. Artificial Intelligence and Law 10, 237\u2013260 (2002). https:\/\/doi.org\/10.1023\/A:1025450828280<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Them Algorithms based on Artificial Intelligence (AI) have multiple applications and they are currently capable of revealing who likes whom, what music you love, how […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[74],"tags":[],"class_list":["post-5752","post","type-post","status-publish","format-standard","hentry","category-blog-en"],"acf":[],"yoast_head":"\nArtificial Intelligence, the most human of intelligences - CIDAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cidai.eu\/en\/la-intel\u00b7ligencia-artificial-la-mes-humana-de-les-intel\u00b7ligencies\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial Intelligence, the most human of intelligences - 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