Opaque Algorithms Research Articles

Balancing Innovation and Privacy: Safeguarding Personal Information in the AI-Driven Digital Era

The rapid innovation of Artificial Intelligence (AI) has transformed various sectors of society by revolutionising decision making and enhancing efficiency through novel data-driven technologies. This paper explores the challenges of striking a healthy balance between future AI innovation and personal data privacy, where the massive collection and utilisation of personal data have given rise to significant privacy concerns. The study identifies the risks of massive data collection, complex and opaque algorithms, and cybersecurity threats, while simultaneously highlighting the existing legal frameworks such as General Data Protection Regulation (GDPR) and the variations among global approaches to data privacy. The paper also discusses the technical solutions such as privacy-preserving techniques including differential privacy and federated learning, as well as encryption technologies that can facilitate the secure storage and transmission of data. The research proposes strategies for building privacy-preserving AI models and encouraging cross-industry collaboration to achieve a balance between innovation and the protection of individual privacy. It also adds to the ongoing discourse on shaping a responsible future for AI.

Artificial Intelligence in Medicine: Are We Ready?

In spite of my personal belief in the benefits of artificial intelligence (AI), reading Cathy O'Neil's book "Weapons of Math Destruction" left me feeling unsettled.1 She describes how flawed and unchecked algorithms are widely applied in areas that affect us all: hiring, credit scoring, access to education, and insurance pricing. In one example, a fixed percentage of teachers in a U.S. region was dismissed every year based on biased and opaque algorithms. The authors concluded that such algorithms act as "weapons of math destruction," perpetuate and amplify societal biases, act unethically, and harm vulnerable populations. The question arises as to what happens when we apply these algorithms to medicine? How do we know whether we are giving our patients the correct diagnosis or prognosis? Are we still sure that patients are receiving the appropriate treatment? Would we notice if the algorithms were geared more toward the needs of companies (make a lot of money) or health insurance companies (spend as little as possible)? In fact, evidence of bias and inequality of algorithms in medicine is already available.2 Due to these risks, some of my colleagues suggest that AI should be completely banned from medicine.

Navigating ethical challenges of explainable ai in autonomous systems

The rapid integration of autonomous systems, such as vehicles, drones, and robots, into various sectors brings forth significant ethical challenges concerning their decision-making processes. This paper examines the role of Explainable AI (XAI) in addressing these challenges, particularly regarding accountability in the event of accidents and the necessity of human oversight in automated environments. We discuss the critical ethical implications of transparency, emphasizing how XAI can bridge the gap between complex algorithmic decision-making and public understanding, thereby fostering trust in these technologies. The paper also outlines the current regulatory frameworks for AI safety, analysing their effectiveness in promoting responsible innovation. Furthermore, we investigate the consequences of opaque algorithms, especially in life-critical applications where the stakes are exceptionally high. Through an analysis of case studies, we showcase how organizations have successfully implemented XAI to enhance safety measures and uphold ethical responsibility in their autonomous systems. Ultimately, this study advocates for the integration of XAI as a vital component in developing responsible autonomous technologies, ensuring accountability, and safeguarding public trust in an era increasingly defined by automation.

Algorithmic governance or extortion? Everyday experiences of fintech for loans in Nigeria

ABSTRACT This paper aims to understand the everyday experiences of financialised inclusion amongst fintech users in Nigeria. Concerns about the use of fintech to drive financial inclusion in the global south have led social scientific scholars to argue that it extends processes of financialisation by employing opaque algorithms to extract the behavioural data of users in order to inundate them with unsecured credit and monetise their data through resale. The consequences of these practices are not fully understood across various contexts. Using a variety of ethnographic methods, this paper examines patterns of fintech adoption and usage in Jimeta, Nigeria. It demonstrates that while fintech allows people to sort out their personal issues amidst precarity, it is also entangled in algorithmic governance issues that facilitate algorithmic extortion to the detriment of user populations. Ultimately, the paper argues that fintech, contrary to claims about its potential to unlock prosperity, is one that opens up novel possibilities for profit making and accumulation, albeit with severe consequences on user populations.

Large language models for structured reporting in radiology: past, present, and future.

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.

Systematic Literature Review on Gig Economy: Power Dynamics, Worker Autonomy, and the Role of Social Networks

This study explores the dynamics of the collective agency among gig workers in the digital platform economy, focusing on three key research questions. First, it examines power dynamics, worker autonomy, and the role of social networks in mitigating power imbalances imposed by digital platforms. Second, it investigates how algorithmic management affects gig workers’ agency and their capacity for collective action. Lastly, it proposes directions for future research to address power imbalances and enhance worker empowerment. Using a systematic literature review (SLR) and bibliometric analysis of 59 scholarly articles, this study reveals that gig workers, despite the control exerted by opaque algorithms, leverage social networks to enhance their autonomy and bargaining power. These networks enable information sharing, negotiation strategies, and collective actions that challenge platform-driven power asymmetries. The study proposes a comprehensive framework illustrating the interplay of economic, technological, social, and regulatory forces affecting gig workers. These insights offer practical implications for policymakers and platform developers aiming to foster a more equitable gig economy. Future research should explore the long-term impacts on worker well-being and assess the effectiveness of regulatory interventions in addressing power imbalances.

SUSTAINABLE FINANCE AND USE OF ARTIFICIAL INTELLIGENCE IN INVESTMENT DECISION MAKING

Artificial Intelligence (AI) is widely used across various industries to enhance efficiency, optimize decision-making, and create innovative solutions and it is now transforming the field of investment decision-making. As companies and investors place a greater emphasis on ethical and sustainable practices, sustainable finance-which incorporates environmental, social, and governance (ESG) factors into financial activities - is gaining global traction. Artificial Intelligence (AI) is becoming a vital instrument in the sustainable finance, because of its ability to analyse enormous volumes of data and spot patterns that are beyond human comprehension. This research paper examines the use of artificial intelligence (AI) tools like predictive analytics, machine learning, and natural language processing,in the financial decision-making. AI can quickly evaluate ESG aspects, predict long-term sustainability risks, and pinpoint high-impact investment opportunities that are in line with sustainability objectives by automating data gathering and analysis. Furthermore, AI-driven insights promote more ethical and knowledgeable financial decision-making by helping investors better manage risks related to social inequity, resource depletion, and climate change. Even though artificial intelligence (AI) has many benefits for sustainable finance, there are drawbacks as well, including data biases, opaque algorithms, and ethical issues. The article concludes that, artificial intelligence helps in creating more precise, effective, and socially conscious financial strategies.Artificial Intelligence (AI) is becoming increasingly important in sustainable finance decision-making due to its ability to handle complex, large-scale data, identify patterns, and optimize decision-making processes in ways that can advance sustainability goals. However, to fully realise AIs potential in forming a sustainable future for global finance, careful governance and ethical concerns are indispensable.

Do opaque algorithms have functions?

Do opaque algorithms have functions?

Ethical Failures of Generative AI in News Production, Implications and Countermeasures

Generative AI is recognized by the media (people) for its high-quality dialogue, complex reasoning and other emergent capabilities in content generation, and human-machine collaboration will gradually become the norm in news production, with application advantages in news production, news distribution and other news survival links. While generative AI assists news production and promotes innovation in news reporting, it also has problems such as providing inaccurate facts, opaque algorithms, and controversial boundaries of application, etc. For this reason, the media should regulate the ethics of human-computer collaboration in terms of optimizing the editing process, disclosing information about algorithms, and setting the boundaries of application of machines.

Opaque algorithms, transparent biases: Automated content moderation during the Sheikh Jarrah Crisis

Social media platforms, while influential tools for human rights activism, free speech, and mobilization, also bear the influence of corporate ownership and commercial interests. This dual character can lead to clashing interests in the operations of these platforms. This study centers on the May 2021 Sheikh Jarrah events in East Jerusalem, a focal point in the Israeli-Palestinian conflict that garnered global attention. During this period, Palestinian activists and their allies observed and encountered a notable increase in automated content moderation actions, like shadow banning and content removal. We surveyed 201 users who faced content moderation and conducted 12 interviews with political influencers to assess the impact of these practices on activism. Our analysis centers on automated content moderation and transparency, investigating how users and activists perceive the content moderation systems employed by social media platforms, and their opacity. Findings reveal perceived censorship by pro-Palestinian activists due to opaque and obfuscated technological mechanisms of content demotion, complicating harm substantiation and lack of redress mechanisms. We view this difficulty as part of algorithmic harms, in the realm of automated content moderation. This dynamic has far-reaching implications for activism’s future and it raises questions about power centralization in digital spaces.

A Brave New World: AI as a Nascent Regime?

This transdisciplinary discussion draws parallels between Aldous Huxley's dystopian vision in his novel 'Brave New World' and the current era dominated by Large Language Models (LLM) and Generative Artificial Intelligence, scrutinizing its significant implications and potential effects. It explores how AI can influence human behavior, reshape societal norms, and potentially lead to the homogenization of linguistic expression. Highlighting the risks of unregulated AI-driven tools, the discussion stresses their potential to standardize and diminish the richness of human language, creativity, and authentic expression. The transformative potential of AI across various domains is recognized, with a strong emphasis on ethical considerations, democratic governance, and preserving core human values. Using 'Brave New World' as a literary framework, it advocates for a transdisciplinary dialogue to critically evaluate AI's impact on social ethos, logos, and pathos. The discussion underscores a global collective responsibility to utilize AI to enrich shared human experiences without compromising the nuances that define our identity and autonomy. It addresses the consequences of uniformity and stability, the role of technology and consumerism, emotional experiences, deep relationships, personal growth, and the threat of totalitarian control, with AI emerging as a nascent regime powered by opaque algorithms implemented by transnational, global companies. Serving as both a reflection on humanity's role in an automated age and a call to action, it advocates for a technological deployment that enhances, rather than diminishes, our inherent humanity.

Evaluating Transparency and Explainability in AI-Driven Planning and Scheduling: A Comprehensive Literature Review

As AI technologies rapidly integrate into our work environments, the need for systems that effectively collaborate with humans grows. A crucial aspect of this interaction is the explainability of AI behavior to human users. While AI has surpassed human decision-making abilities in many areas, its opaque algorithms often produce accurate yet incomprehensible results. This lack of transparency can hinder trust between humans and machines. The TUPLES project, a three-year Horizon Europe research initiative, seeks to address this by developing AI-based planning and scheduling tools with a human-centered focus. By utilizing both data-driven and symbolic AI methods, TUPLES aims to create scalable, transparent, robust, and secure systems. The project employs a use-case-oriented approach, drawing from industry expertise to ensure relevance in areas such as manufacturing, aviation, and waste management. The EU guidelines for Trustworthy AI emphasize key principles like human oversight, transparency, fairness, societal well-being, and accountability. The Assessment List for Trustworthy Artificial Intelligence (ALTAI) serves as a selfassessment tool for evaluating AI systems. Current planning and scheduling tools only partially meet these criteria, highlighting the need for innovative AI development. Our literature review explored the transparency and explainability of AI algorithms in planning and scheduling, identifying metrics and recommendations. The study underscores the role of Explainable AI (XAI) in addressing the black box problem, using techniques that make AI systems more understandable and accurate. XAI relies on human explanation concepts to ensure a human-centered approach, offering specific methods and guidance for selecting appropriate tools in planning and scheduling contexts.

The News Crawler: A Big Data Approach to Local Information Ecosystems

In the past 20 years, Silicon Valley’s platforms and opaque algorithms have increasingly influenced civic discourse, helping Facebook, Twitter, and others extract and consolidate the revenues generated<em>. </em>That trend has reduced the profitability of local news organizations, but not the importance of locally created news reporting in residents’ day-to-day lives. The disruption of the economics and distribution of news has reduced, scattered, and diversified local news sources (digital-first newspapers, digital-only newsrooms, and television and radio broadcasters publishing online), making it difficult to inventory and understand the information health of communities, individually and in aggregate. Analysis of this national trend is often based on the geolocation of known news outlets as a proxy for community coverage. This measure does not accurately estimate the quality, scale, or diversity of topics provided to the community. This project is developing a scalable, semi-automated approach to describe digital news content along journalism-quality-focused standards. We propose identifying representative corpora and applying machine learning and natural language processing to estimate the extent to which news articles engage in multiple journalistic dimensions, including geographic relevancy, critical information needs, and equity of coverage.

Strengthening the European Union by Regulating the Digital Single Market

Polarization of the society is nowadays easier than ever due to the strong influence of social media. Opaque algorithms personalize news feed of users through massive data processing and thus creating effects that are fueling extremization of opinions. Negative effects of social media can be used by third parties to influence society to achieve their goals, however antidemocratic. Digital Markets Act and Digital Services Act aim to regulate Digital Single Market through fair competition and consumer protection regulation. This regulation can have significant impact on the democratic deficit of the European Union as it has potential to eradicate analyzed negative effects of social media on the polarization of society.

Stratified Public Connections—Beyond the Taste for News?

ABSTRACT Many scholars have expressed concerns about the well-being of modern societies in the age of digitalisation and datafication, with opaque algorithms on social media supposedly structuring human agency and eroding the foundations of democracy. To address these concerns, I conducted multiple correspondence analysis (MCA) on a rich body of representative survey data on Danish citizens (N = 5660) to examine how citizens connect to and engage within the public sphere, thus providing a sociological contextualisation for understanding the complex interplays between news consumption, broader media usage, cultural lifestyles, and patterns of social inequality. The results showed how tendencies of mistrust and political alienation are not distinctive of social media users; rather, such challenges should be understood through the notion of social stratification. This finding contributes to the discussion on inequality in terms of access to and participation in the public sphere.

Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models

On the issue of insurance discrimination, a grey area in regulation has resulted from the growing use of big data analytics by insurance companies: direct discrimination is prohibited, but indirect discrimination using proxies or more complex and opaque algorithms is not clearly specified or assessed. This phenomenon has recently attracted the attention of insurance regulators all over the world. Meanwhile, various fairness criteria have been proposed and flourished in the machine learning literature with the rapid growth of artificial intelligence (AI) in the past decade and have mostly focused on classification decisions. In this article, we introduce some fairness criteria that are potentially applicable to insurance pricing as a regression problem to the actuarial field, match them with different levels of potential and existing antidiscrimination regulations, and implement them into a series of existing and newly proposed antidiscrimination insurance pricing models, using both generalized linear models (GLMs) and Extreme Gradient Boosting (XGBoost). Our empirical analysis compares the outcome of different models via the fairness–accuracy trade-off and shows their impact on adverse selection and solidarity.

Speaking out on the internet: what does it mean to seek “justice” on social media?

The public sphere has moved online, and with it, several versions of “protests” and “movements” too. We are living through a moment in history where the “due process” of law and justice may or may not follow the public demonstration of either injustice or democratic dissent. A “public” protestation in such a situation has very often meant “on social media”, giving even more credence to the idea that a successful protest is often one that “trends”. This paper will address some of these issues in the context of India, where the spread of internet and social media in the past several years has been as quick as it has been widespread. Using two examples – that of the Indian #MeToo movement and the Anti-CAA protest at Shaheen Bagh – this paper attempts to trace the complexities of online movements in India that have grown online and spread offline. The many inequalities of access, class, caste, language, and internet literacy that exist in India provide a background for these movements and the idea of seeking justice. The complexity of opaque algorithms that curate social media feeds makes the endeavour of seeking for and finding various forms of justice or solidarity – especially through the internet – even more complicated. The two movements – one that was born online and the other that was entirely offline; both operationalised the internet and particularly social media in ways that gave them more visibility and popularity. Both movements were led by women, but one was mostly a movement of elites, while the other was led by those usually considered outside the mainstream. Using insights from Science Technology and Society (STS), this research effort hopes that reading these two cases together will be able to reveal some of the intricacies of the tricky nature of online justice seeking and the complex Indian digital space.

Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare

In modern life, the application of artificial intelligence (AI) has promoted the implementation of data-driven algorithms in high-stakes domains, such as healthcare. However, it is becoming increasingly challenging for humans to understand the working and reasoning of these complex and opaque algorithms. For AI to support essential decisions in these domains, specific ethical issues need to be addressed to prevent the misinterpretation of AI, which may have severe consequences for humans. However, little research has been published on guidelines that systematically addresses ethical issues when AI techniques are applied in healthcare. In this systematic literature review, we aimed to provide an overview of ethical concerns and related strategies that are currently identified when applying AI in healthcare. The review, which followed the PRISMA guidelines, revealed 12 main ethical issues: justice and fairness, freedom and autonomy, privacy, transparency, patient safety and cyber security, trust, beneficence, responsibility, solidarity, sustainability, dignity, and conflicts. In addition to these 12 main ethical issues, we derived 19 ethical sub-issues and associated strategies from the literature.

The Importance of Informative Interventions in a Wicked Environment

The Importance of Informative Interventions in a Wicked Environment

Pathway of Mathematical Optimization Research: From Specialized Problems and Opaque Algorithms to Standardized Problems and Transparent Algorithms

Mathematical optimization (MO) formulates a decision problem with a maximization or minimization objective and a set of constraints on the decision variables, and designs an algorithm to find the best solution [...]