Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food waste and enhance the circular economy and discusses the current state of food waste and the circular economy, highlighting specific ways that AI can be used to monitor and optimise food production and supply chains, redistribute excess food to those in need, and support circular economy initiatives. As a result, we can maximise resource efficiency and minimise environmental impact with these applications, ultimately creating a more sustainable and equitable food system.

/pdf/using-artificial-intelligence-to-tackle-food-waste-and-vlgh26jw.pdf

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

The integration of artificial intelligence (AI) into human-robot interaction (HRI) has witnessed significant advancements in recent years, revolutionizing the way humans and robots collaborate and coexist. This review provides a comprehensive overview of the latest breakthroughs in AI-driven HRI and identifies the challenges that lie ahead. Recent years have seen a surge in AI-driven capabilities that enhance human-robot interaction. Machine learning algorithms enable robots to adapt to user preferences and behaviors, creating personalized and intuitive interactions. Natural language processing (NLP) facilitates seamless communication between humans and robots, enabling voice commands and context-aware responses. Computer vision advancements empower robots with enhanced perception, enabling them to recognize and interpret human gestures, emotions, and facial expressions. Reinforcement learning has played a pivotal role in enabling robots to learn from human feedback and optimize their actions in real-time. Socially assistive robots leverage AI to provide emotional support and companionship, particularly in healthcare and elderly care settings. Despite these advancements, challenges persist in the field of AI-driven HRI. Ethical considerations, including privacy concerns and the responsible use of AI in influencing human behavior, demand careful attention. Ensuring the safety and security of AI-driven robotic systems remains paramount, requiring robust measures against malicious attacks and unintended consequences. Human-robot trust remains a critical challenge, necessitating transparent AI algorithms and effective communication strategies. Interdisciplinary collaboration between AI researchers, roboticists, psychologists, and ethicists is essential to address the complex socio-technical aspects of HRI. The fusion of AI and human-robot interaction holds immense potential to redefine various facets of our daily lives. This review highlights recent strides in AI-driven HRI, emphasizing the need for interdisciplinary efforts to address challenges and ensure the responsible development and deployment of AI-powered robotic systems. As researchers continue to innovate, the dynamic landscape of AI and human-robot interaction promises a future where seamless collaboration and coexistence between humans and robots become an integral part of our societal fabric. 

AI and human-robot interaction: A review of recent advances and challenges

As artificial intelligence (AI) continues to permeate various aspects of our lives, the ethical challenges associated with its development become increasingly apparent. This paper navigates and reviews the ethical dilemmas in AI development, focusing on strategies to promote transparency, fairness, and accountability. The rapid growth of AI technology has given rise to concerns related to bias, lack of transparency, and the need for clear accountability mechanisms. In this exploration, we delve into the intricate ethical landscape of AI, examining issues such as bias and fairness, lack of transparency, and the challenges associated with accountability. To address these concerns, we propose strategies for transparency, including the implementation of Explainable AI (XAI), advocating for open data sharing, and embracing ethical AI frameworks. Furthermore, we explore strategies to promote fairness in AI algorithms, emphasizing the importance of fairness metrics, diverse training data, and continuous monitoring for iterative improvement. Additionally, the paper delves into strategies to ensure accountability in AI development, considering regulatory measures, ethical AI governance, and the incorporation of human-in-the-loop approaches. To provide practical insights, case studies and real-world examples are analyzed to distill lessons learned and best practices. The paper concludes with a comprehensive overview of the proposed strategies, emphasizing the importance of balancing innovation with ethical responsibility in the evolving landscape of AI development. This work contributes to the ongoing discourse on AI ethics, offering a roadmap for navigating the challenges and fostering responsible AI development practices.

Navigating and reviewing ethical dilemmas in AI development: Strategies for transparency, fairness, and accountability

The rapid integration of artificial intelligence (AI) systems into various domains has raised concerns about their impact on individual and societal wellbeing, particularly due to the lack of transparency and accountability in their decision-making processes. This review aims to provide an overview of the key legal and ethical challenges associated with implementing transparency and accountability in AI systems. The review identifies four main thematic areas: technical approaches, legal and regulatory frameworks, ethical and societal considerations, and interdisciplinary and multi-stakeholder approaches. By synthesizing the current state of research and proposing key strategies for policymakers, this review contributes to the ongoing discourse on responsible AI governance and lays the foundation for future research in this critical area. Ultimately, the goal is to promote individual and societal wellbeing by ensuring that AI systems are developed and deployed in a transparent, accountable, and ethical manner. 

Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making

The rapid advancement of artificial intelligence (AI) technologies has prompted significant societal, ethical, and legal concerns regarding their deployment in information technology (IT) systems. Addressing these concerns necessitates the establishment of a robust ethical framework to guide AI development and integration into IT systems. This paper presents a comprehensive conceptual framework aimed at fostering ethical AI development within IT systems. The proposed framework incorporates multidisciplinary perspectives, drawing upon principles from ethics, computer science, law, and philosophy. It emphasizes the integration of ethical considerations at every stage of the AI development lifecycle, including design, implementation, deployment, and maintenance. Central to this framework is the recognition of AI systems as socio-technical artifacts with profound impacts on individuals, communities, and societies at large. Key components of the framework include transparency, accountability, fairness, privacy, and security. Transparency entails ensuring that AI algorithms and decision-making processes are comprehensible and explainable to stakeholders, thereby fostering trust and enabling scrutiny. Accountability mechanisms are essential for attributing responsibility for AI-driven outcomes and facilitating recourse in cases of harm or injustice. Moreover, the framework emphasizes the importance of fairness in AI systems, advocating for the mitigation of biases and discrimination across diverse demographic groups. Privacy protection measures are deemed crucial to safeguarding individuals' personal data from unauthorized access or misuse, while robust security protocols are essential for defending against malicious exploitation and adversarial attacks. By delineating ethical guidelines and best practices, this conceptual framework aims to empower developers, policymakers, and organizations to navigate the complex ethical landscape of AI development in IT systems. Ultimately, the adoption of such a framework is imperative for harnessing the transformative potential of AI technologies while upholding fundamental ethical principles and societal values.

https://wjarr.com/sites/default/files/WJARR-2024-0735.pdf

Towards a conceptual framework for ethical AI development in IT systems

/pdf/mobile-computer-vision-based-applications-for-food-2qh7yk55.pdf

Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

Abstract Although numerous ethical principles and guidelines have been proposed to guide the development of artificial intelligence (AI) systems, it has proven difficult to translate these principles into actionable practices beyond mere adherence to ethical ideas. This is particularly challenging in the context of AI systems for healthcare, which requires balancing the potential benefits of the solution against the risks to patients and the wider community, including minorities and underserved populations. To address this challenge, we propose a shift from one-size-fits-all ethical principles to contextualized case-based ethical frameworks. This study uses an AI-enabled mHealth application as a case study. Our framework is built on existing ethical guidelines and principles, including the AI4People framework, the EU High-Level Expert Group on trustworthy AI, and wider human rights considerations. Additionally, we incorporate relational perspectives to address human value concerns and moral tensions between individual rights and public health. Our approach is based on ”ethics by design,” where ethical principles are integrated throughout the entire AI development pipeline, ensuring that ethical considerations are not an afterthought but implemented from the beginning. For our case study, we identified 7 ethical principles: fairness, agility, precision, safeguarding humanity, respect for others, trust and accountability, and robustness and reproducibility. We believe that the best way to mitigate and address ethical consequences is by implementing ethical principles in the software development processes that developers commonly use. Finally, we provide examples of how our case-based framework can be applied in practice, using examples of AI-driven mobile applications in healthcare. 

Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications

Introduction With the growing prevalence of AI-based systems and the development of specific regulations and standardizations in response, accountability for consequences resulting from the development or use of these technologies becomes increasingly important. However, concrete strategies and approaches of solving related challenges seem to not have been suitably developed for or communicated with AI practitioners. Methods Studying how risk governance methods can be (re)used to administer AI accountability, we aim at contributing to closing this gap. We chose an exploratory workshop-based methodology to investigate current challenges for accountability and risk management approaches raised by AI practitioners from academia and industry. Results and Discussion Our interactive study design revealed various insights on which aspects do or do not work for handling risks of AI in practice. From the gathered perspectives, we derived 5 required characteristics for AI risk management methodologies (balance, extendability, representation, transparency and long-term orientation) and determined demands for clarification and action (e.g., for the definition of risk and accountabilities or standardization of risk governance and management) in the effort to move AI accountability from a conceptual stage to industry practice.

/pdf/investigating-accountability-for-artificial-intelligence-3124dffh.pdf

Investigating accountability for Artificial Intelligence through risk governance: A workshop-based exploratory study

As we look towards the future of healthcare, integrating Care Robots (CRs) into health systems is a practical approach to address challenges such as an ageing population and caregiver shortages. However, ethical discussions about the impact of CRs on patients, caregivers, healthcare systems, and society are crucial. This normative research seeks to define an integrative and comprehensive ethical framework for CRs, encompassing a wide range of AI-related issues in healthcare. To build the framework, we combine principles of beneficence, non-maleficence, autonomy, justice, and explainability by integrating the AI4People framework for a Good AI Society and the traditional bioethics perspective. Using the integrated framework, we conduct an ethical assessment of CRs. Next, we identify three key ethical trade-offs and propose remediation strategies for the technology. Finally, we offer design recommendations for responsible development and usage of CRs. In conclusion, our research highlights the critical need for sector-specific ethical discussions in healthcare to fully grasp the potential implications of integrating AI technology. 

Beyond the Metal Flesh: Understanding the Intersection between Bio- and AI Ethics for Robotics in Healthcare

White Paper On a Risk-Based Assessment Approach to AI Ethics Governance

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Auxane Boch

Author Tools

Chat about Author