Ethical Management of AI-Produced Digital Works

 

Dr. Avinash Dhavan ¹, Yuvraj Parmar ², Shikha Gupta ³, Ashish Verma ⁴ , Dr. Varsha Agarwal ⁵, Dr. Thangaraja T ⁶

 

¹ Assistant Professor, Bharati Vidyapeeth Institute of Management Studies and Research, Specialization: HR and O.B, Navi Mumbai, India

² Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India

³ Assistant Professor, School of Business Management, Noida International University 203201, India

⁴ Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India

⁵ Associate Professor, ISME - School of Management and Entrepreneurship, ATLAS Skill Tech University, Mumbai, Maharashtra, India

⁶ Assistant Professor Grade -II, Department of Management, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU) Tamil Nadu, India

 

 

 

 

1. INTRODUCTION

The advent of artificial intelligence (AI) as an imaginative co-worker is a radical shift in the nature of the conception, creation, and consumption of digital works. Visually, writing, and design prototypes are becoming more and more closer to the edges of human and machine creativity, through algorithmically generated paintings and music compositions, to the creation of virtual influencers and design prototypes. This change presents enormous ethical, legal, and social issues requiring proper consideration and appropriate handling. The emergence of the AI-created digital content is not only a technological event but also a cultural and ethical change that forces society to re-evaluate some of the primary questions about authorship, ownership, creativity and responsibility. In the center of this change is the fact that AI systems can be used to mimic human creativity. With sophisticated machine learning, natural language processing and generative adversarial networks (GANs), AI is capable of generating original and in many cases unpredictable results that can compete with and sometimes outperform human imagination in specific fields Casalone (2020). Yet, this ability leads to one of the urgent questions: what or who is the author of the work created by AI? Conventional models of authorship are based on human will, ethical action, and creative representation something the AI cannot possess in the traditional meaning. This means that the need to assign AI systems ownership and moral responsibility puts ethical and legal principles of creative industries to the test. Along with authorship, there are also complicated ethical implications of AI creative ability in the context of transparency, consent and fairness Benanti (2023). Countless AI systems are trained with large volumes of data that contain copyrighted, sensitive, or culturally valuable information and in many instances it is not clearly stated and or recognized that the original creators were involved. This makes some questions concerning the ethical validity of AI training practice and the possibility of using human creativity. Also, the black box problem, also known as AI decision-making transparency, makes it difficult to hold AI-generated works responsible and accountable Bartlett et al. (2022). According to Figure 1, there are interrelated ethical, legal, and social aspects of AI creativity. In the absence of clear mechanisms, it is hard to establish the ways the AI systems come up with their results or whether they reproduce bias, plagiarism or misinformation.

                                                                         Figure 1

Figure 1 Comprehensive Framework for the Ethical Management of AI-Produced Digital Works

 

Besides, the spread of AI-generated content has enormous consequences on creative labor markets, as well as cultural production. On the one hand, AI creates an unprecedented chance to democratize the creative process, and this means that people with no official artistic education can create sophisticated pieces using the available tools. Conversely, it poses a threat to oust the creative professionals and shift creative power to large technology companies that can afford the most sophisticated AI systems and data Hill et al. (2022) This is where the tension of democratization and monopolization arises, necessitating ethical guidelines to allow a fair involvement and avoid exploitation. The current legal and regulatory frameworks have a hard time keeping up with the fast-developing AI creativity globally. The current intellectual property (IP) regulations have been tailored towards human authors and are not readily adaptable to non-humans or machine-aided generation Krishnapriya et al. (2020). Consequently, loopholes in law, licensure, and attribution remain to create confusion and probable inter-jurisdictional clashes. These problems can only be resolved by law reform but also a moral obligation of fairness, inclusiveness and accountability.

 

2. Conceptual Framework

2.1. DEFINITION OF AI-PRODUCED DIGITAL WORKS

AI-generated digital art is any creative work of art produced wholly or in part by artificial intelligence systems by way of analyzing data, recognizing patterns, and modeling creative work. Such works can consist of pictures, sound, literature, film script, and even digital interactivity. As compared to the traditional creative processes, where the human imagination and manual work are the only tools available, AI-generation works are created based on the algorithms, which may learn on a huge amount of data available and independently generate new works Rhim et al. (2021). The creations produced by AI can be divided into three main categories: fully autonomous creations, in which AI can create outputs by itself; semi autonomous creations, in which human creators interact with AI tools in an iterative process; and AI-assisted creations, where AI is simply considered a supplement to human creativity and/or provides technical services or optimization. This range points to the intricate interaction of the intent of humanity with the results of the machine. Such works are not described simply as technical but rather as philosophical since they blur the line between machine automation and human expression. With AI, its creativeness is not the result of intuition and experience but probabilistic modeling since it cannot be characterized as conscious, intentional, and emotive Tang et al. (2023). Hence, AI-generated digital works are not unique due to the cognition of AI but rather reproduce and mimic creative behaviors by the use of trained associations between data. Such controversies transform the definition of art production and art enjoyment in the online era Acquaviva et al. (2024).

 

2.2. OVERVIEW OF CURRENT AI TECHNOLOGIES IN CREATIVE INDUSTRIES

Artificial intelligence technologies have entered many fields of creativity and transformed the concept of artistic creation, music, and literature, and design perception and distribution. Generative adversarial networks (GANs), state-of-the-art text-to-image models like DALL•E, Midjourney, and Stable Diffusion, and similar visual art systems generate very detailed and stylistically varied images based on textual input. Using huge datasets these systems are able to learn visual patterns so that they can create realistic portraits, surreal compositions, and even imitate classical styles [9]. Applied in the field of music, AI software such as AIVA, Jukebox, and Amper Music create music of various genres based on learning harmonic and rhythmic rules and assists the composers and content creators. NLP (natural language processing) models, like GPT-based systems, have been applied in literature and journalism, to produce articles, poetry, and screenplays that are written in a human linguistic structure and style of narration. Correspondingly, in motion pictures and animation, AI will be used to help in scriptwriting, scene creation, voice synthesis, and visual effect improvement. The fashion and design industries utilize AI to forecast trends, optimization of production, and the development of new product designs, whereas the gaming industry is making use of AI to develop adaptive storytelling and procedural world-building Morley et al. (2021).  All these technologies democratize the means of creative expression since they reduce entry barriers and at the same time concentrate creative power in the hands of those who have control over data and computational resources.

 

2.3. RELATIONSHIP BETWEEN AI, CREATIVITY, AND AUTHORSHIP

The connection between AI, creativity and authorship is one of the most complicated and controversial intersections in the modern ethics and aesthetics. The emergence of generative AI systems is recalibrating creativity, a long-held human quality that is based on imagination and emotional intelligence and intentionality. Such technologies are able to simulate creative behavior through analyzing patterns, recombining data, and producing some novel outputs. Nevertheless, the creativeness of AI is not original- it develops through computational learning as opposed to conscious creation Li et al. (2023). This is in contrast with philosophical and legal definitions of what can be considered true creative authorship. Traditional models of authorship include moral and intellectual property of a thought, based on personal will and expression. AI systems, in turn, do not have agency, consciousness and moral responsibility. Hence, putting AI as the author of any work of art confuses the distinction between a tool and a creator Van Wynsberghe (2021). Authorship can be attributed to the human programmer, who worked on the data, the data curator, who worked on the data, or the user, who worked on the data, but there is no agreed way of distributing credit or blame. This imprecision goes further to the cultural and ethical realms. When AI creates art that triggers an emotional or meaning response, audiences can think of it as creative but its roots in algorithmic synthesis bring up the issue of authenticity and artistic value. Table 1 presents major research on ethical, legal and creative implications De Mauro and Pacifico (2024). With the further development of AI, the correlation between human intent and machine production needs to be redefined, not as the substitution of human imagination but as the novel enhanced version of collaborative authorship, which incorporates human imagination and the innovation of computation.

Table 1

 

3. Ethical Considerations in AI-Generated Content

3.1. AUTHORSHIP AND OWNERSHIP DILEMMAS

Authorship and ownership of AI-generated content is still one of the most controversial ethical issues of the digital age. Conventional conceptions of authorship are based on the human imagination, purpose, and ethics, all of which are absent in artificial intelligence by definition. Despite their high originality and aesthetic output, AI systems do not work based on the conscious choice, but on the basis of algorithmic learning. This is the main difference that makes it difficult to assign ownership in the creative act Kshetri (2024). When a work of art, literature, or music is generated by an AI system, it is possible that many different people will be considered the creators of the work: the developer that made the algorithm, the user that gave prompts or input data, or the company that owns the technology and data used. In the absence of clear structures, such competing claims cause conflict about moral rights, profit distribution and credit due diligence. Moreover, copyright regulations in most countries limit them to human authors, making AI-created works either belong to the public domain or be not covered by the traditional standards. This ambiguity is ethically questionable because it can take away the creativity of humans because creators will be deprived of credit to the machines.

 

3.2. ISSUES OF TRANSPARENCY AND ACCOUNTABILITY

The two essential elements of the ethical governance of AI-generated content are transparency and accountability. Even current AI systems, especially those based on deep learning and generative models tend to be black-box in that they can be difficult to understand how a particular output is generated even by their creators.

                                                                          Figure 2

 

Such opaque nature is an ethical challenge of traceability as it becomes hard to determine the fairness, lack of bias and intellectual property theft in the content generated. In AI, Figure 2 indicates the biggest transparency issues and accountability systems. Absence of transparency compromises the faith in the population and poses some concern to accountability. In case an AI-created work of art violates copyright, disseminates false information, or reinforces negative stereotypes, it is not clear who is to be held accountable the developer of AI, the data supplier, or the direct user. This proliferation of responsibility provides loopholes in ethical and legal terms, making it possible to utilize/misuse or exploit this without consequences. Besides, these risks are intensified by AI-generated deepfakes and artificial media, which erodes authenticity in digital communication by obliterating the difference between reality and fake.

 

3.3. DATA PRIVACY AND CONSENT IN AI TRAINING DATASETS

The quality and the integrity of the training datasets is fundamental to AI-generated content. Nevertheless, these data collections frequently include massive volumes of copyright, personal or culturally sensitive information gathered online without express consent. This comes with serious ethical issues of data privacy, consent, and ownership. By being trained on such data, an AI model is, in a way, recycling human creativity and labor into novel outputs, which is uncompensated or not accredited at all, which is almost akin to digital appropriation. Privacy wise, the face of those whose information, be it in the form of pictures, voices, or texts, is used to train generative models, can be reproduced without their approval. Such unnecessarily use of personal data may cause reputational damage, identity theft, or emotional trauma. Moreover, any biases in the training data may carry repeat thematic patterns in AI results which undermine fairness and inclusivity. The development of AI should be ethically responsible, which means that it should be transparent in sourcing data, informed consent, and respect the intellectual property rights. The developers are encouraged to use licensed, open-source or ethically-reviewed datasets that adhere to the standards of privacy and cultural attentiveness.

 

4. Legal and Regulatory Challenges

4.1. INTELLECTUAL PROPERTY AND COPYRIGHT IMPLICATIONS

The advent of AI-created content has revealed some deep cracks in the current intellectual property (IP) and copyright regulations. The conventional copyright law is based on the assumption concerning human authorship, according to which creative expression is produced through personal intellect and will. But AI is not conscious, has no emotional experience, and is not a legal person, which is why it is challenging to attribute authorship and ownership to the machine itself. This means that AI-generated content is not usually covered by the regular laws and, therefore, there is always a gray area on who owns it. Most jurisdictions only offer copyright to human creators. This puts the works that are produced by AI unsecured or owned by the human or organization that did the programming, the running or the deployment of the AI system. As an example, the copyright legislation of the United States expressly refuses to grant registration to works that are created by a machine without human contribution. In a similar manner, the UK and the EU recognize the existence of computer-generated works, but the rights are vested in the individual who set the creative process. This contradiction makes it more difficult to issue rights internationally and enforce them. Such loopholes, morally, threaten to discourage human innovation and allow companies to exploit humans by using AI platforms that have been trained on the intellectual property of others. Anonymity is another impediment to accountability because AI reproduces or changes copyrighted work without authorization.

 

4.2. LICENSING AND ATTRIBUTION REQUIREMENTS

The issues of licensing and attribution of AI-generated works are controversial in terms of legal and ethical aspects, and they are mostly associated with copycatting and collaboration of AI creativity. Generative AI systems are trained on large collections of copyrighted, open-access and public domain content. Whenever these models reproduce/remix their training data, there are issues of who should be credited with distribution of credit. Devoid of explicit licensing, the creators of work that is used to train a dataset do not receive any payment or credit, even though they have an indirect role in the final output. During the commercialization of AI tools licensing is also a controversial issue. As an example, when the artist applies an AI model that was trained on thousands of pieces of art, does he/she/they or the developers of the AI model license the original creators? Lack of standard frameworks has resulted into conflict and lawsuits especially in visual arts and music. On the ethical level, fair attribution would be ensuring the integrity of creative work and acknowledging human involvement in the basis of the generative force of AI. Some of the solutions can be the introduction of transparent systems of data provenance, the creation of the ethics AI license, and the disclosure in the case of AI contribution to the creative work.

 

4.3. INTERNATIONAL DIFFERENCES IN AI REGULATION

The regulation of AI is diverse, depending on the approach to the regulation of technologies in various jurisdictions and their cultural, legislative, and ethical aspects. Such discrepancies produce discrepancies in the treatment of AI-generated works according to the intellectual property and data protection laws. By way of example, the European Union has taken a holistic approach by passing various laws such as the AI Act that focuses on transparency, accountability, and risk based categorization of AI systems. The EU contemplates AI also in its General Data Protection Regulation (GDPR), which guarantees the privacy and agreement in AI training. The US, on the other hand, is more decentralized and innovativeness based. Although the U.S. Copyright Office does not protect non-human works, it permits the registration of copyright on the works that entail adequate human authorship in AI-assisted creation. This case-by-case analysis, nevertheless, is not uniform and puts AI-generated works in legal uncertainty. In the meantime, the flexible policy promoting AI development and trying out adaptive copyright models has been adopted in countries like Japan or South Korea. China, in its turn, is creating AI-specific legislation, which focuses on state regulation and control over the content.

 

 

 

 

 

5. Socioeconomic Impacts

5.1. EFFECTS ON CREATIVE PROFESSIONALS AND LABOR MARKETS

The adoption of AI by creative sectors has impacted creative practitioners and the employment sector greatly. On the one hand, AI is efficient, lowers the cost of production, and expands the creative options, but, on the other hand, it endangers the traditional occupations in the art, design, journalism, music, and entertainment industry. Much of the individuals in the profession are scared of being made obsolete because the generative AI systems are capable of creating content within minutes that would have taken humans skills and effort. To give a few examples, the artists and copywriters, as well as composers, have an increasing competition with the AI tools that can produce similar results at a minimum price. Nevertheless, the impact of AI is not at all negative. It opens up new job opportunities in AI art direction, prompt engineering, digital ethics and algorithm design. Those creative professionals that accept AI as a working tool can enhance their output and experiment in the area of creativity. However, the unequal access to and literacy in technology worsens disparities- in favor of the persons with resources to utilize AI effectively. Outdated creativity-based jobs are not done away with, but instead the labor market is restructured broadly. The management of AI is thus a must to make sure it is used to provide a complement to human creativity and not to substitute it. Displacement can be reduced by policies to facilitate fair labor transitions and reskilling, as well as protecting the creative rights.

 

5.2. DEMOCRATIZATION VS. MONOPOLIZATION OF DIGITAL CREATIVITY

AI technologies can make creativity democratic by making the barriers to entry less significant, allowing people with no formal knowledge to create professional-quality art, music, and design. The platforms like ChatGPT, Midjourney, and Runway are accessible and allow people without artistic background to explore the type of artistic work, which helps to establish inclusivity and innovation. This democratization increases the involvement in creative culture and diversification of the world artistic scene. On the other hand, the identical technologies are a threat of intensifying monopolization of digital creativity. The vast majority of the most powerful AI models are operated by massive technological corporations with special access to data, computing resources, and algorithms. This is the concentration of creative tools and datasets that strengthens corporate domination, reduces transparency and access by independent creators. Further, AI-generated content conditioned on open-source information tends to sell off the creativity of the masses without any fair payment, which is the essential values of the fair cultural exchange. The conflict between democratization and monopolization puts the dissimilarity between the creation and the control of AI to the forefront, demonstrating a very important socioeconomic gap. Open-source innovation, equal sharing of data, and equal access to the platform should therefore be given a priority in ethical governance. Incentives To encourage decentralized AI ecosystems, creative centralization can be avoided and global participation encouraged. Finally, to maintain a thriving digital culture, it is necessary to find a balance between inclusivity and responsibility so that AI becomes a shared creative commons and not an instrument of corporate concentration.

 

5.3. PUBLIC PERCEPTION AND CULTURAL INFLUENCE OF AI WORKS

There is fascination, skepticism, and moral concern that influence the way AI-generated works are perceived by the general population. The fact that AI can create art that is capable of replicating human emotion and cleverness astonishes the eyes of many viewers, who consider AI to be a revolutionary combination of technology and art. Yet, some consider AI-driven production as the fake copies that do not possess any form of authenticity, emotion, or cultural relevance. This ambivalence is open to the wider social issues of originality, meaning and significance of human experience in art. The cultural production and consumption is also transformed by the increasing power of AI. Algorithms creativity complicates the hierarchies of taste of the traditional approach which makes artistic creation more commodified yet more available. The spread of AI-generated media is a potential threat to overcrowding the audience with homogenized or derivative content, with the boundary between a real expression of a person and an automated synthesis becoming unclear. The way AI creativity is perceived by society and how it is affected by culture is illustrated in Figure 3. Moreover, AI application to the cultural heritage reproduction and digital restoration evokes ethical concerns regarding authenticity and ownership, as well as historical interpretation.

 

                                                                         Figure 3 

Figure 3 Model of Cultural and Public Responses to AI Creativity

 

AI can be culturally used as a reflector and a generator, as on the one hand, it reflects all the values of society in the training data, and on the other hand, it creates new aesthetic standards. It has an effect on creativity that goes beyond art into politics, education, and entertainment and changes the perception of creativity itself. Ethical AI use in culture should thus strive to enhance transparency, inclusivity and respect to diversity whereby technology should not take the culture away but should enhance it.

 

6. Frameworks for Ethical Management

6.1. ETHICAL DESIGN PRINCIPLES FOR AI DEVELOPERS

The ethical management of digital works created with the help of AI starts with the responsible design practice at the developmental stage. The developers of AI have a central role in ensuring that the systems they develop have moral values and social responsibility. Principles of ethical designs should act as guiding principles and these include fairness, accountability, transparency, and respect to human autonomy. The value sensitive design should be an important consideration of developers so that the algorithms capture ethical considerations and cause minimal harm. Human-centricity is a key concept in the design of AI systems, and AI systems must support human creativity rather than destroy it. The developers need to have measures against bias to curate and represent the data and perform fairness tests. Also, the traceability and accountability are encouraged by clear documentation of the data sources and model behavior. The interdisciplinary teams, which are made up of ethicists, artists and social scientists, in the development process will also guarantee that technical innovation is not lightened by ethical reasoning. Creative applications of AI must have built-in consent-informed systems to confirm the authenticity of the training data to avoid using copyrighted or personal data. It is also the responsibility of developers to create AI-based systems that would be sensitive to cultural diversity and not to homogenize the expression of creativity.

 

6.2. ROLE OF TRANSPARENCY, AUDITING, AND ACCOUNTABILITY SYSTEMS

Ethical governance on the foundation of AI-generated creativity lies in the system of transparency, auditing, and accountability. Transparency will mean that the AI systems are operated in a way that is understandable, traceable, and verifiable which will enable stakeholders to understand how outputs are generated and on what data they are based. Users and audiences are able to make judgments of authenticity and trustworthiness when they are aware that a particular piece of content is AI-generated. Extensive auditing systems are also important to ensure that ethical and legal practices are adhered to.

                                                                         Figure 4  

Figure 4 Multilayered Structure for Transparent and Accountable AI Practices

 

Algorithms audits may produce biases, copyright infringements, or unethical data practices in AI systems that can be detected through regular algorithmic audits. Developers and independent third-party evaluations of the internal audits serve to keep the credibility and trust of the population. Figure 4 illustrates the built layers of transparency, auditing and accountability in AI. Furthermore, the transparency of information about the source of data, the updating of the model, and risk analysis should be provided in the form of transparency reports that will increase accountability and social responsibility. The accountability systems specify the responsibility owners of AI-generated results. Having distinct roles defined between developers, platform providers and users would guarantee that liability may be shared equally in case of misuse or damage. Ethical responsibility ought to be taken to a higher level and that is the level of moral responsibility, which is based on adhering to justice, respect, and human dignity.

 

7. Future Directions and Recommendations

7.1. ETHICAL STANDARDS FOR AI CREATIVITY

It is important to develop ethical guidelines that will regulate AI creativity to make sure that technological innovation should be aligned with human values and cultural integrity. Increasing the involvement of AI in the production of art, literature, and media, it is necessary to have universal ethical standards governing the design, implementation, and use of AI. These standards must be independent of falsehood and bias, and should respect humanity and authorship, as well as encourage inclusiveness and social accountability. A set of ethics should underline the anthropocentricity of creativity- it should be made clear that AI is not to substitute human imagination but to enhance it. Data integrity is also a subject of standards required of AI systems which must be based on the legally and ethically obtained material, the consent and copyright of which is respected. There should be standardized mechanisms of proper attribution and recognition of human and machine input in the creative sectors. International community bodies like UNESCO, WIPO and ISO can be at the forefront of codifying international ethical standards to facilitate international cooperation and harmonization of the law.

 

7.2. POLICY SUGGESTIONS FOR FAIR AND RESPONSIBLE INNOVATION

Policy frameworks are very important in creating a responsible and fair environment of AI-driven innovation. Governments and other international organizations should implement progressive policies that balance innovative freedom and responsibility as well as protection of human rights. Meanwhile, one of the priorities of the policy should be to make the intellectual property laws clearer to accommodate AI-generated works and ensure that the human authorship is not violated and that the creators are not exploiting the data. Ethical data governance should also be promoted by the policy makers, requiring that the collection of the data, consent mechanism, and the decision-making with the help of the algorithms are made transparent. The democratization of access to the innovative technologies by the large corporations can be achieved through incentivizing open-source AI projects and equal-footing data-sharing platforms. Moreover, the governments ought to invest in ethical AI and creative workforce displacement and cultural protection research. Accountability AI regulations defining the responsibility of developers, users, and organizations will allow responsible AI usage and avoid misuse, including the production of plagiarism or deepfakes. Having public labeling of AI-generated material would also serve to increase transparency and awareness of the audience.

 

7.3. RESEARCH NEEDS AND INTERDISCIPLINARY COLLABORATION

The ethical management of AI-generated digital art in the future relies on the high-quality research and collaboration between disciplines. The art, technology, ethics, and law have an intersection that requires a comprehensive approach that cannot be accomplished by any individual discipline. To create the frameworks that would focus on the moral, cultural, and societal implications of the AI creativity, the cooperation of computer scientists, ethicists, legal scholars, social scientists, and artists is crucial. Research must be focused on the explainable and interpretable AI which would allow transparency in creative processes and accountability. Research of bias reduction, data ethics, and cultural representation in AI-generated content is also essential. Additionally, equitable and inclusive policy formulation can be made based on sociological studies on how AI changes creative identity, perception by the audience, and artistic work. The innovation that should be encouraged in academic and industry partnerships must be in line with the moral values that is, with the help of joint laboratories, grants, cross-sector think tanks directed at responsible AI. International cooperation can be used to create common standards and best practices especially in intellectual property reform and in ethical use of data.

 

8. Conclusion

One of the most significant challenges and opportunities of the twenty-first century is the ethical management of the AI-produced version of digital works. With the continued development of artificial intelligence as a creative partner and a technological disruptor, humanity will also have to redefine the traditional concepts of authorship, creativity, and ownership. Although the ability of AI to produce art, literature, and design helps broaden the scope of human imagination, it also reveals the weaknesses of human law, ethics, and society that were never intended to accommodate the non-human creator. The challenges that are being faced need to be addressed in a multidimensional approach, based on fairness, transparency, accountability and respect of the human dignity. Making AI responsible, meaning designing it to be value-sensitive, practicing data ethically by consent, and creating equitable relationships with creative technologies should be ethical management. The legal changes are necessary to enlighten the ownership and attribution of AI-generated works, between innovation and safeguarding human creators. Organizations and companies need to develop a system of governance that would be transparent, audited, and accountable in terms of ethics throughout the lifecycle of AI. No less significant are cultural and societal aspects. The artificial creativity produced by AIs must add value to human creativity, not substitute it, and inclusivity and diversity of digital art should be promoted

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

REFERENCES

Acquaviva, V., Barnes, E. A., Gagne, D. J., McKinley, G. A., and Thais, S. (2024). Ethics in climate AI: From Theory to Practice. PLOS Climate, 3, e0000465.

Benanti, P. (2023). The Urgency of an Algorethics. Discover Artificial Intelligence, 3, 11.

Bartlett, R., Morse, A., Stanton, R., and Wallace, N. (2022). Consumer-lending Discrimination in the FinTech era. Journal of Financial Economics, 143, 30–56.  

Casalone, C. (2020). Una Ricerca Etica Condivisa Nell’era Digitale. La Civiltà Cattolica, 2, 30–43.

De Mauro, A., and Pacifico, M. (2024). Data-Driven Transformation: Maximise Business Value with Data Analytics (The FT Guide). FT Publishing.

Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P. (2024). Generative AI. Business and Information Systems Engineering, 66, 111–126.

Giudici, P., Centurelli, M., and Turchetta, S. (2024). Artificial Intelligence Risk Measurement. Expert Systems with Applications, 235, 121220.

Hill, D., O’Connor, C. D., and Slane, A. (2022). Police Use of Facial Recognition Technology: The Potential for Engaging the Public Through Co-Constructed Policy-Making. International Journal of Police Science and Management, 24, 325–335.

Kshetri, N. (2024). Economics of Artificial Intelligence Governance. Computer, 57, 113–118.

Krishnapriya, K. S., Vítor, A., Kushal, V., Michael, K., and Kevin, B. (2020). Issues Related to Face Recognition Accuracy Varying Based on Race and Skin Tone. IEEE Transactions on Technology and Society, 1, 8–20.

Li, F., Ruijs, N., and Lu, Y. (2023). Ethics and AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare. AI, 4, 28–53. https://doi.org/10.3390/ai4010003       

Morley, J., Elhalal, A., Garcia, F., Kinsey, L., Mokander, J., and Floridi, L. (2021). Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. Minds and Machines, 31, 239–256.  https://doi.org/10.1007/s11023-020-09531-3   

Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Lui, V., and Gibson, J. (2021). Artificial Intelligence for Good Health: A Scoping Review of the Ethics Literature. BMC Medical Ethics, 22, 14.  https://doi.org/10.1186/s12910-021-00589-0   

Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., and Floridi, L. (2024). Taking AI Risks Seriously: A New Assessment Model for the AI Act. AI and Society, 39, 2493–2497.  https://doi.org/10.1007/s00146-023-01772-4

Ricciardi Celsi, L. (2023). The Dilemma of Rapid AI Advancements: Striking a Balance between Innovation and Regulation by Pursuing Risk-Aware Value Creation. Information, 14, 645

Rhim, J., Lee, J.-H., Chen, M., and Lim, A. (2021). A Deeper Look at Autonomous Vehicle Ethics: An Integrative Ethical Decision-Making Framework to Explain Moral Pluralism. Frontiers in Robotics and AI, 8, Article 2021.  https://doi.org/10.3389/frobt.2021.702843

Tang, L., Li, J., and Fantus, S. (2023). Medical Artificial Intelligence Ethics: A Systematic Review of Empirical Studies. Digital Health, 9, 20552076231186064. https://doi.org/10.1177/20552076231186064

Van Wynsberghe, A. (2021). Sustainable AI: AI for Sustainability and the Sustainability of AI. AI and Ethics, 1, 213–218.  https://doi.org/10.1007/s43681-021-00024-1