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Explainable Artificial Intelligence: A Main Foundation in Human-centered AI

Posted: March 30th, 2022 | Author: | Filed under: Artificial Intelligence, Human-centered explainable AI | Tags: , , , , | Comments Off on Explainable Artificial Intelligence: A Main Foundation in Human-centered AI

Human-centered explainable AI (HCXAI) is an approach that puts the human at the center of technology design. It develops a holistic understanding of “who” the human is by considering the interplay of values, interpersonal dynamics, and the socially situated nature of AI systems.

Explainable AI (XAI) refers to artificial intelligence -and particularly machine learning techniques- that can provide human-understandable justification for their output behavior. Much of the previous and current work on explainable AI has focused on interpretability, which can be viewed as a property of machine-learned models that dictates the degree to which a human user—AI expert or non-expert user—can come to conclusions about the performance of the model given specific inputs. 

An important distinction between interpretability and explanation generation is that explanation does not necessarily elucidate precisely how a model works, but aims to provide useful information for practitioners and users in an accessible manner. The challenges of designing and evaluating “black-boxed” AI systems depends crucially on “who” the human in the loop is. Understanding the “who” is crucial because it governs what the explanation requirements are. It also scopes how the data are collected, what data can be collected, and the most effective way of describing the why behind an action.

Explainable AI (XAI) techniques can be applied to AI blackbox models in order to obtain post-hoc explanations, based on the information that they grant. For Pr. Dr. Corcho, rule extraction belongs to the group of post-hoc XAI techniques. This group of techniques are applied over an already trained ML model -generally a blackbox one- in order to explain the decision frontier inferred by using the input features to obtain the predictions. Rule extraction techniques are further differentiated into two subgroups: model specific and model-agnostic. Model specific techniques generate the rules based on specific information from the trained model, while model-agnostic ones only use the input and output information from the trained model, hence they can be applied to any other model. Post-hoc XAI techniques in general are then differentiated depending on whether they provide local explanations -explanations for a particular data point- or global ones -explanations for the whole model. Most rule extraction techniques have the advantage of providing explanations for both cases at the same time.

The researchers Carvalho, Pereira, and Cardoso have defined a taxonomy of properties that should be considered in the individual explanations generated by XAI techniques:

  • Accuracy: It is related to the usage of the explanations to predict the output using unseen data by the model. 
  • Fidelity: It refers to how well the explanations approximate the underlying model. The explanations will have high fidelity if their predictions are constantly similar to the ones obtained by the blackbox model.
  • Consistency: It refers to the similarity of the explanations obtained over two different models trained over the same input data set. High consistency appears when the explanations obtained from the two models are similar. However, a low consistency may not be a bad result since the models may be extracting different valid patterns from the same data set due to the ‘‘Rashomon Effect’’ -seemingly contradictory information is fact telling the same from different perspectives. 
  • Stability: It measures how similar the explanations obtained are for similar data points. Opposed to consistency, stability measures the similarity of explanations using the same underlying model. 
  • Comprehensibility: This metric is related to how well a human will understand the explanation. Due to this, it is a very difficult metric to define mathematically, since it is affected by many subjective elements related to human’s perception such as context, background, prior knowledge, etc. However, there are some objective elements that can be considered in order to measure ‘‘comprehensibility’’, such as whether the explanations are based on the original features (or based on synthetic ones generated after them), the length of the explanations (how many features they include), or the number of explanations generated (i.e. in the case of global explanations). 
  • Certainty: It refers to whether the explanations include the certainty of the model about the prediction or not (i.e. a metric score). 
  • Importance: Some XAI methods that use features for their explanations include a weight associated with the relative importance of each of those features. 
  • Novelty: Some explanations may include whether the data point to be explained comes from a region of the feature space that is far away from the distribution of the training data. This is something important to consider in many cases, since the explanation may not be reliable due to the fact that the data point to be explained is very different from the ones used to generate the explanations. 
  • Representativeness: It measures how many instances are covered by the explanation. Explanations can go from explaining a whole model (i.e. weights in linear regression) to only be able to explain one data point.

In the realm of psychology there are three kinds of views of explanations: 

  • The formal-logical view: an explanation is like a deductive proof, given some propositions.
  • The ontological view: events – state of affairs – explain other events.
  • The pragmatic view: an explanation needs to be understandable by the ‘‘demander’’. 

Explanations that are sound from a formal-logical or ontological view, but leave the demander in the dark, are not considered good explanations. For example, a very long chain of logical steps or events (e.g. hundreds) without any additional structure can hardly be considered a good explanation for a person, simply because he or she will lose track. 

On top of this, the level of explanation refers to whether the explanation is given at a high-level or more detailed level. The right level depends on the knowledge and the need of the demander: he or she may be satisfied with some parts of the explanation happening at the higher level, while other parts need to be at a more detailed level. The kind of explanation refers to notions like causal explanations and mechanistic explanations. Causal explanations provide the causal relationship between events but without explaining how they come about: a kind of ‘‘why’’ question. For instance, smoking causes cancer. A mechanistic explanation would explain the mechanism whereby smoking causes cancer: a kind of ‘‘how’’ question.

As said, a satisfactory explanation does not exist by itself, but depends on the demander’s need. In the context of machine learning algorithms, several typical demanders of explainable algorithms can be distinguished: 

  • Domain experts: those are the ‘‘professional’’ users of the model, such as medical doctors who have a need to understand the workings of the model before they can accept and use the model.
  • Regulators, external and internal auditors: like the domain experts, those demanders need to understand the workings of the model in order to certify its compliance with company policies or existing laws and regulations. 
  • Practitioners: professionals that use the model in the field where they take users’ input and apply the model, and subsequently communicate the result to the users’ situations, such as  for instance loan applications. 
  • Redress authorities: the designated competent authority to verify that an algorithmic decision for a specific case is compliant with the existing laws and regulations. 
  • Users: people to whom the algorithms are applied and that need an explanation of the result. 
  • Data scientists, developers: technical people who develop or reuse the models and need to understand the inner workings in detail.

Summing up, for explainable AI to be effective, the final consumers (people) of the explanations need to be duly considered when designing HCXAI systems. AI systems are only truly regarded as “working” when their operation can be narrated in intentional vocabulary, using words whose meaning go beyond the mathematical structures. When an AI system “works” in this broader sense, it is clearly a discursive construction, not just a mathematical fact, and the discursive construction succeeds only if the community assents.

China: Techno-socialism Seasoned with Artificial Intelligence

Posted: March 18th, 2022 | Author: | Filed under: Artificial Intelligence, Book Summaries, Realpolitik | Tags: , , , | Comments Off on China: Techno-socialism Seasoned with Artificial Intelligence

People take the great ruler for granted and are oblivious to his presence.The good ruler is loved and acclaimed by his subjects. The mediocre ruler is universally feared. The bad ruler is generally despised. Because he lacks credibility, the subjects do not trust him. On the other hand, the great ruler seldom issues orders. Yet he appears to accomplish everything effortlessly. To his subjects everything he does is just a natural occurrence.

Tao-Tê-Ching, Lao-Tsé

Anyone who wants to learn something about China today, to know its strategic plan between now and 2050, the means to achieve it, and what drives this country in this titanic effort, should read the book El gran sueño de China: tecno-socialismo y capitalismo de estado by Claudio F. González.

Claudio F. González, PhD in engineering and economist, has lived in China, as director of Asia for the Polytechnic University of Madrid (UPM), for six years. During this time he has been involved in the fields of education, entrepreneurship, research and innovation in the Asian giant. From this privileged vantage point he has been able to observe, analyze, and understand the complexity of this country.

According to the author, throughout the 20th century, the Western world looked at China with the condescension that is due to a former empire in decline and mired in chaos, power struggles, and poverty, and only in the last decades of the past century, as a market of great potential and a cheap manufacturer of limited quality. Nonetheless, China had -and has- its plan, the ultimate goal of which is returning the “Empire of the Center” to the place it has held for most of human history. Namely: being the most socially and technologically advanced nation and, from there, regaining world leadership in the economic, commercial, and cultural spheres.

In 2015, the government announced the first of its grand plans – Made in China 2025, with the goal of making China by this date a leader in industries such as robotics, semiconductor manufacturing, electronic vehicles, renewable energy and, of course, artificial intelligence.

Initiatives such as the Belt and Road Initiative (BRI) or institutions such as the Asian Infrastructures Investment Bank (AIIB) are nothing more than instruments through which China wants to reshape an international order that is more favorable to its new interests. One of China’s stated goals is that by 2035 it wants to be the country that globally sets the next standards in areas such as AI, 5G or the Internet of Things.

China’s successes in the digital economy are based on three main factors:

1. A market that is both huge in size and young, which allows for the rapid commercialization of new business models and equally allows for a high level of experimentation.

2. An increasingly rich and varied innovation ecosystem that goes far beyond a few large and famous companies.

3. And a strong government support, which provides favorable economic and regulatory conditions, and also acts as a venture capital investor, a consumer of products based on new technologies and produced by local companies, and allows access to data that are key to developing new solutions in conditions that are unthinkable in other regions.

Professor F. González calls this model techno-socialism or state capitalism.

What are the defining characteristics of this techno-socialist model?

China intends to harness the interest in technological development of its own industry to align it with government interests. The overall goal is, starting from what the Chinese Communist Party (CCP) calls a moderately prosperous socialist society, to catch up with and surpass the most developed Western countries, ideally by the 100th anniversary of the founding of the People’s Republic of China (2049). Socialism in the sense of the Chinese regime is no longer socialism in the traditional sense of ownership and collective management of the means of production, a political conception definitively defeated after Mao’s demise; but its control and coordination to achieve social objectives.

The features that characterize this techno-socialism are those of complete physical security for people and things, the absence of extreme poverty, full employment, and the possibility for the most industrious to obtain economic and prestige rewards for their efforts, as long as they are aligned with the objectives established by the party and do not put its dominion at the least risk. This techno-socialism tries to lead society as a whole towards a centrality of thought that avoids extremisms that destroy peace and social security, and that do not call into question the leadership and omnipotent dominance of the party.

The alignment between business interests -or those of other institutions- and public interests, as interpreted by the party, creates a unique innovation ecosystem in which companies capable of promoting solutions for a broad user base become champions of an industrial policy. Once this status is achieved, and always within the logic of interest alignment, they will gain access to a whole arsenal of measures -subsidies, tax reductions, preferential treatment-, to maintain this position and, if possible, extend it internationally, since they are no longer merely companies, but ambassadors of a new model. In the particular case of artificial intelligence, the government has contributed with the necessary conditions -strategies, plans, regulation, space for experimentations- and practical support -venture capital, public procurement, permissions to access data-, for innovations in this field to follow. Alibaba, Tencent, and Baidu set up research centers, deploy applications, enroll human capital, and support CCP policies.

Will techno-socialism be able to generate enough disruptive innovations to give the technology created in China an entity of its own?

Between 2015 and 2018, the venture capital funded more than €1 trillion to new technology start-ups in China. China has more unicorns -companies less than ten years old with a valuation above $1 billion- than any other country. In terms of research, China is already the country with the most scientific articles, surpassing the US, although it is true that its impact is still minor, the gap is rapidly closing. It turns out that it has been the state the one which, with its research grants, scholarships and universities, has generated ideas that, because of their risk, private initiative would never have dared to finance. Hence, in this sense, public authorities that nurture alternative ways of thinking are the true engine of progress.

Professor F. González names this innovation paradigm applicable to China as asymmetric triple helix model, in which the national government controls the overall innovation context through its top- down policies and plans but, at the same time, allows a certain level of autonomy for district, local and regional governments to conduct their own experiments and accommodate innovations that emerge from the bottom up. Large companies, start-ups, and finance companies are aligned with government interests. And universities and research centers similarly align themselves with government objectives in producing new knowledge and generating talent in the form of human capital.

And eventually, when will China achieve and assume the role of world leader?

From the author’s standpoint China, due to a set of inconsistencies and structural gaps, is neither ready nor willing to assume the global leadership in the foreseeable future. However, it does claim to be the most powerful and influential economy, with the most cohesive society and the least contested domestic leadership that will enable it to become something like the best country in a fragmented world. China’s current strength lies in the existence of a long-term plan: a sense of destiny that ties in with its imperial past. There is a deep conviction in Chinese society, a determination, which is the key force to achieve these strategic objectives.

China does not want to be a powerful nation, but deserves it.

Democratizing Artificial Intelligence in the Banking Industry

Posted: June 8th, 2021 | Author: | Filed under: Artificial Intelligence | Tags: , , , , , | Comments Off on Democratizing Artificial Intelligence in the Banking Industry

A white paper -published together with Redesigning Financial Services and EY- about how AI can be used to tackle some daily problems the banking institutions have to cope with such as, amongst others, anti-money laundering, KYC, data quality management, process & data mining…

Here is the direct link to download the report:

Democratization or Industrialization: the AI Crossroads

Posted: March 2nd, 2021 | Author: | Filed under: Artificial Intelligence | Tags: , | Comments Off on Democratization or Industrialization: the AI Crossroads

“Yes, but artificial intelligence must become common currency”.

A few days ago I had the good fortune to attend a meeting between technology investors, entrepreneurs, businessmen and professors in the field, the latter three, of AI. It was interesting, on the one hand, to mix in the same virtual space money, willingness to create something, success in having done so, and knowledge… and, on the other hand, to observe the same vital dilemma regarding this technology is shared in the background: democratization or industrialization of artificial intelligence.

Information and communication technologies -and more specifically AI- are GPTs, general purpose technologies, a term coined by MIT professors Erik Brynjolfsson and Andrew McAfee in their book The Second Machine Age; namely, technologies that “disrupt and accelerate the normal march of economic progress”. The steam engine and electricity were also GPTs. They were disruptive technologies that have extended their reach into many corners of the economy and radically altered the way we live and work.

Nonetheless, if we look at the current state of AI, it has yet to take off. Why? One of the reasons is perhaps because it is stuck at a crossroads.

On the one hand, we have tech giants like Amazon, Google, Facebook, Alibaba, Tencent… they are not only competing with each other to see which is the first to discover the next disruptive breakthrough within AI. At the same time, they compete against fast AI startups that want to use machine learning, deep learning, ontologies or even hybrid approaches -mathematics, statistics, rule-based programming and logic…-, to revolutionize certain specific industries. It is a competition between two approaches to extend AI in the field of economics: the industrialization of the powerful giants versus the democratization of the agile startups. How that race plays out will determine the nature of the AI business landscape: monopoly, oligopoly, or free and spontaneous competition amongst thousands of companies. The industrialization approach wants to turn AI into a commodity, with a price tending towards zero. Its goal is to transform the power of AI, and its various subfields, into a standardized freemium service; namely, any company can acquire it, with its use perhaps being free of charge for academic or personal environments. Access to this freemium AI environment would be through cloud platforms. The powerful giants behind these platforms (Google, Alibaba, Amazon…) act as service companies, managing the network and charging a fee. Connecting to that network would allow traditional companies, with a large data set, to leverage the optimization power of AI without having to redo their entire business. The most obvious example of this approach: Google’s TensorFlow. This is an open-source software ecosystem for building deep learning models; however, it still requires specialized programming skills to make it work. The goal of the network approach is to both lower that specialization threshold and increase the functionality of AI platforms in the cloud. Making full use of an AI model is not easy as of today but AI giants hope to simplify this technology and then reap the rewards, in addition to operating the network.

On the other hand, AI start-ups and middle-sized enterprises (MsEs) are taking the opposite approach. Instead of waiting for this network to take shape, they are creating AI niche products for each use case. Such startups and MsEs are aiming at specialization, rather than breadth. Instead of providing, for example, natural language processing models for general purposes, they build new products, solutions, niche platforms for algorithms to perform specific tasks such as fraud tracking, insurance policy comparison, customer profiling for upselling and cross-selling, terrorist threat detection on social networks, pharma knowledge graph generation… The starting postulates of these startups and MsEs are twofold: on the one hand, traditional businesses are still very far, operationally, from being able to use a multipurpose AI network; on the other hand, AI should start to be an intrinsic element in the business operation of these traditional companies. It is because of the latter that, almost always, companies following this approach end up building a strategic relationship with the AI startup or MsE, which has introduced them to this world.

Who will win in this race? Difficult to make a prediction. What is clear is that, if the industrialization approach triumphs, the astronomical economic benefits of this technology will be concentrated in a handful of companies (probably American and Chinese ones); if the democratization approach succeeds, these huge benefits will be spread among thousands of vibrant young agile companies.

Play ball, ladies and gentlemen, and stay tuned!

To Overcome the Reluctance for Accepting AI, We Must Highlight the Gains in Terms of Productivity and Efficiency, Using Plain Language.

Posted: January 28th, 2021 | Author: | Filed under: Artificial Intelligence, Interviews | Tags: , , , | Comments Off on To Overcome the Reluctance for Accepting AI, We Must Highlight the Gains in Terms of Productivity and Efficiency, Using Plain Language.

As a welcome for the allocated seats in the Redesigning Financial Services Strategic Steering Committee,’s Chief Operating Officer Gabriele Donino, and the Managing Director Switzerland Domingo Senise de Gracia were interviewed to talk about the use of artificial intelligence, the potentials, opportunities and barriers.

Link to the interview.