Table of Contents

Artificial Intelligence (AI) has become a disruptive technology that is changing the way work is done, decisions made and innovations introduced(Varghese, Raj and Venkatesh, 2022). This introduction prepares the reader for the main topics to be discussed throughout the report, which are the ethical aspect of Applied AI, its sustainability issues, and the social consequences.

Advanced technologies such as machine learning, neural networks, and others have made it possible to automate several processes, make predictions, and make decisions based on data(Bharadiya, 2023). The use of AI is growing exponentially for which it has some severe ethical issues such as privacy, fairness, responsibility, and social justice(Zhou et al., 2020). In addition, the possibility of AI systems being sustainable from an energy and long-term operational perspective raises additional considerations.

1.1 Background and Significance of Applied AI

AI has been incorporated in different fields like health, finance, transport, and city planning, changing business and working processes (Jarrahi, 2018). Applied AI is a concept related to the implementation of AI in business and daily life to improve efficiency and create new opportunities. This section discusses the development of AI as an idea, as well as a reality in the world today with the help of examples of industries and effects on society.

AI has now entered the mainstream and has contributed to digitalization processes, as well as generating discussions on what should be the ethical and sustainable behaviour that should be followed when designing and implementing AI (Vrontis et al., 2022). For this reason it becomes crucial for organizations and policymakers to understand these implications in order to properly mitigate and take full advantage of the benefits of AI systems in a safe manner.

1.2 Objectives of the Report

The overall objective of this report is, thus, to critically assess Applied AI with regards to the ethical and sustainable framework. Specifically, the report aims to achieve the following objectives:

Define the scope and use of Applied AI in various fields, focusing on its capacity for change and the difficulties that come with it.

Explain the following ethical principles in the creation of AI, namely; transparency, accountability, fairness, and robustness. Explain the following ethical issues concerning the use of AI decision-making systems.

Explore the social, economic, and environmental consequences of the use of AI technologies. Discuss the prospects of sustainable uses of AI in renewable energy sources, farming, and city planning with their advantages and disadvantages.

Propose guidelines for the correct and fair use of AI, noting that ethical standards, laws, and regulation, as well as sustainable solutions, should be used to achieve AI’s fair and lasting integration.

2 Understanding Applied AI

AI can be defined as the ability of machines, especially computers, to perform tasks that are smart like human beings (Wang and Siau, 2019). Some of them include learning process, reasoning process, and self correcting process. Applied AI is more concerned with employing these capabilities to address various challenges in different fields, thus making AI a functional technology to advance effectiveness, creativity, and choices.

The spectrum of applied AI includes such technologies as machine learning, natural language processing, computer vision, and robotics. Each of these technologies has distinct capabilities:

· Machine Learning: Includes procedures where computers get to learn from the data and then are capable of making decisions. They are used in recommendation systems, and in general, any field that requires predictive analysis(Kour and Gondhi, 2020).

· Natural Language Processing: Helps machines to translate human language and comprehend it. Some examples of AI applications include; chatbots, translators, and sentiment analysis (Torfi et al., 2020).

· Computer Vision: Specializes in the capability of teaching machines to analyze and make conclusions based on the visions. Some of the uses include face identification, medical image analysis, and self-driving cars (Feng et al., 2019).

· Robotics: Combines AI with physical machines to allow them to work on their own. This is especially the case in manufacturing industries, health and services industries (Panesar et al., 2019).

Figure 1: Technologies in Applied AI

The possibilities of applied AI are vast, yet the system’s strengths are constrained by ethical, technical, and pragmatic concerns. Ethical issues are related to privacy, fairness, and explainability of the model’s decision-making. Technically, AI systems are data and computationally intensive in terms of their design and implementation. In practice, their implementation and incorporation into an existing system can be a lengthy and expensive process.

2.1 Key Applications in Various Industries

2.1.1 Healthcare

AI is transforming the healthcare sector through improving the diagnosis, tailoring treatment and overall performance of the health system(Eduard Fosch-Villaronga, 2021). Key applications include:

Figure 2: Applications of AI in Healthcare

2.1.1.1 Medical Imaging:

AI algorithms are used to diagnose diseases with a high degree of accuracy from the images of the human body, including tumours, fractures, and infections (Lotan, 2021; Soun et al., 2021; Syed et al., 2010). For instance, Google Health’s AI in diagnosing breast cancer in mammograms is more accurate than human radiologists.

2.1.1.2 Predictive Analytics:

AI diagnoses or prognosis of patients using data from previous patients and assists the clinicians in decision making (Khanra et al., 2020) . The IBM Watson Health is an AI-based application that is used to predict the health status of a patient and suggest actions to be taken in case the patient’s condition worsens.

2.1.1.3 Telemedicine:

AI chatbots and virtual assistants help in telemedicine, patient screening, and providing consultation, which increases the availability of healthcare services (Jiménez-Serrano, Tortajada and García-Gómez, 2015).

2.1.2 Finance

In the finance industry, AI is applied widely in risk assessment, fraud prevention, and customer service improvementt (Milana and Ashta, 2021). Notable applications include:

2.1.2.1 Algorithmic Trading:

AI systems use market data to conduct trades at the right time and hence, make the best returns as well as minimize losses (Ashish, 2019). Some of the areas where AI is used include; Goldman Sachs uses AI in high-frequency trading.

2.1.2.2 Fraud Detection:

AI models to recognize fraudulent transactions based on the patterns and behaviour that differs from the standard (Abdallah, Maarof and Zainal, 2016). Machine learning is used in PayPal to minimize the false positive fraud and increase the fraud detection ratio (Bao, Hilary and Ke, 2022).

2.1.3 Customer Service:

Use of AI in the form of chatbots and virtual assistants to help customers with their questions, to perform administrative tasks, and to improve clients’ satisfaction (Yi and Liu, 2020). Erica is a similar tool by Bank of America that offers advice and assistance to customers.

Figure 3: Applications of AI in Finance

2.1.4 Transportation

AI is helping in reinventing transport system through the ways of enhancing safety, productivity and utility. Key applications include:

Figure 4: APPLICATIONS OF AI IN Transportation

2.1.4.1 Autonomous Vehicles:

Self-driving cars, which are AI driven, maneuver and make choices with the help of data from the sensors and cameras (Bagloee et al., 2016). Several organizations such as Tesla and Waymo are pushing for this technology with an agenda of minimizing on accidents and traffic jam.

2.1.4.2 Traffic Management:

AI helps in the efficient management of traffic by using traffic cameras, sensors and GPS devices to collect data (Dalal et al., 2023). Other cities such as Los Angeles employ AI in the regulation of traffic signals and even in the estimation of traffic flow.

2.1.4.3 Predictive Maintenance:

Using historical and current data, AI anticipates when a car will require maintenance, thus minimizing the time cars are off the road and the expenses for repairs (Hossain et al., 2022). Such companies as Lufthansa apply AI to predict and solve the problems related to aircraft maintenance to avoid delays.

2.2 Future Directions

Further development in the future of applied AI is expected, including the use of quantum computing, edge AI, and explainable AI. Quantum computing could potentially increase the processing power a thousand-fold, which could lead to denser models of AI (Enad and Mohammed, 2023). This new form of AI, which operates on the device and not on a centralized server, offers quicker decision-making and improved security. XAI is the process of explaining AI decision-making processes so that the reasons behind them can be understood, hence enhancing trust and dealing with ethically sensitive issues (Christianlauer, 2022).

3 Ethical Considerations in Applied AI

3.1 Fundamental Principles of AI Ethics

As AI systems are implemented in all areas of society, they must be ethical and accountable (Jobin, Ienca and Vayena, 2019). Ethics in AI creation and use are guided by explicit and accurate disclosure, responsibility, impartiality, and soundness.

3.1.1 Transparency

AI transparency is how well AI decision-making and operations are explained. It describes how users and others learn about an AI system's procedures and decisions(Benefo et al., 2022). Transparency is important for various reasons:

· When users know how an AI system or algorithm makes a decision, they are more likely to trust it.

· Opportunistic structures help identify faults and biases, making developer and operator punishment easier.

· Transparency helps comply with legal and regulatory frameworks like the EU's General Data Protection Regulation (GDPR), which grants individuals the opportunity to explain automated choices.

3.1.2 Accountability

AI accountability includes holding AI system operators accountable. This principle is essential for trust and ethics (Tsamados et al., 2021). Important accountability factors include:

· Determining who is responsible for what in the AI system and who is legally liable for its effects.

· Maintaining documentation of AI system training data, algorithms, and decision-making for audit and review.

· Legal accountability of AI systems entails determining how to handle harm or errors caused by AI systems and who will pay for them.

3.1.3 Fairness

It is worth using the term ‘fair AI’ to describe the model that it should not aggravate discrimination and prejudice (Zhou et al., 2020). Fairness comprises:

• Ensuring that your AI services and products are not biased with the assistance of race, gender or any other factor.

• What is data, algorithms and finding bias, and how can we decrease it? This involves using other data sets, prejudice identification programs as well as periodically conducting checks on the AI system for prejudice.

• Creating AI solutions for nearly all its clients and specifically for the diverse persons of color.

3.1.4 Robustness

AI robustness is the capacity of AI to function optimally and securely in different situations (Blasimme and Vayena, 2019). This principle is needed to avoid negative outcomes in the process and for AI systems to be able to operate on something or input they have not been designed to deal with. Important aspects:

• To guarantee the AI systems are functional and effective.

• Protect AI systems from adversarial inputs that might cause a compromise of its function or even compromise the AI system itself.

• Developing the AI systems that would be capable of functioning in the environment that can be considered dangerous for people and their property.

3.2 Challenges of Bias and Fairness

Despite the recent advancements in AI, bias as well as fairness are still the ethical and social challenges (Grybauskas, Stefanini and Ghobakhloo, 2022). Such issues need to be addressed to develop effective and impartial artificial intelligence applications.

3.2.1 Data bias

The first and perhaps the most frequent in AI systems is data bias (Norori et al., 2021). Data bias can come from many sources, including:

• Historical bias: Maintaining modern form of social injustice and racism in historical records.

• Sampling Bias: When the training data set is limited to a population or a scenario, the sampling bias is experienced.

• Measurement bias: Occurring as a result of mistakes in data collection or lack of consistency between the data collected.

3.2.2 Algorithm bias

Because of how they examine data, algorithms can also prejudice outcomes(Akter et al., 2021). This can happen via:

· Model Design: Systematic bias in algorithm design and parameters.

· Feature Selection: Biases that result from choosing model features that may not cover all parameters.

3.2.3 Social Impact

The societal impact of biased AI systems can be substantial, leading to:

· Continued or worsened discrimination against vulnerable groups in employment, credit, and policing (Benefo et al., 2022).

· Reducing resources and opportunities for the less privileged, perpetuating social and economic inequality.

3.3 Privacy and Data Protection Implications

Privacy and data protection are key ethical considerations when employing AI systems. AI technologies employ vast data, thus protecting privacy and data responsibly is vital.

3.3.1 Consent and Data Collection

Data collecting for AI processes may involve personal data, which raises privacy concerns. This is because data gathering techniques must be explicit and participants must understand the repercussions (Paweł Socha, 2024). This involves:

· Informed Consent: People should know what data is being gathered, how it will be used, and the repercussions. Consent should be clear.

· Data minimization: Limiting AI system data to what is needed for its purpose to reduce privacy concerns.

3.3.2 Data Security

Data security is needed to protect privacy (De la Torre Díez, García-Zapirain and Lopez-Coronado, 2017). This requires preventative measures against data breaches, unauthorized access, and misuse. Important aspects:

· Protecting stored and transferred data over the internet.

· Anonymous and pseudonymized data are less likely to identify a person.

3.3.3 Ethical Implications

Privacy and data protection go beyond legal compliance (Nishi et al., 2022). They consider the broader impact of data activities on individuals and society:

Surveillance Concerns: The significant usage of AI and data collecting can lead to issues of surveillance and loss of control over one's life, especially when data is used to monitor people.

Trust and Transparency: Protecting user privacy is important in AI technology development since it builds public trust. Users must be informed about data policies and practices.

3.3.4 Ethical Dilemmas in Autonomous Decision-Making

Autonomous decision-making systems like self-driving cars, drones, and AI-driven medical diagnostics raise ethical concerns (Bagloee et al., 2016). These systems are mostly autonomous and make their own decisions, raising ethical concerns.

3.3.4.1 Decision-making criteria

How to choose choice criteria is a fundamental ethical concern in autonomous decision making. Some important factors are:

Setting guidelines for autonomous systems' right and incorrect decisions and acts (Grigorescu et al., 2020). Should a self-driving automobile prioritize pedestrian safety or occupant safety in an inevitable accident?

Ensure AI decision-making values reflect society and ethical norms. This includes safety, fairness, and privacy trade-offs.

3.3.4.2 Accountability and duty

AI self-organization complicates attribution for its activities and results. Important issues:

Determining culpability when an ASH makes a mistake (Hiter, 2023). This could be developers, manufacturers, operators, the AI system, or others.

Ensure self-driving systems' decision-making process is transparent and explainable, especially in sensitive fields like health and police work (Jobin, Ienca and Vayena, 2019). To acquire user and stakeholder trust and hold someone accountable for actions, this is crucial.

3.4 Real-World Examples

Real-world examples show the ethical issues surrounding autonomous decision-making:

Self-Driving Cars: The use of self-driving cars by Tesla and Waymo raises ethical concerns about safety, blame, and decision-making during accidents (Hossain et al., 2022). Deadly self-driving car accidents have emphasized the need of ethics.

Autonomous Weapons: Military organizations' employment of autonomous weapons systems for lethal reasons raises moral questions about force, responsibility, and battle provocation.

AI in Healthcare: IBM Watson for Oncology is an AI-based medical diagnosis tool that raises problems regarding AI's openness, explainability, and how much humans should trust AI recommendations.

4 Sustainability in Applied AI

4.1 Social and Economic Implications

Realization of AI in different sectors has social and economical consequences(Nishant, Kennedy and Corbett, 2020). These can be either beneficial or adverse, on employment, inequality, and sustainability of AI innovation in the future.

4.1.1 Employment

AI offers an opportunity to change the labor market through automation of processes, enhancement of human functions, and emergence of new professions (Jobin, Ienca and Vayena, 2019).

4.1.1.1 Job Automation and Displacement

AI systems are particularly effective in handling routine and monotonous tasks; this makes it easier to improve the speed of doing work (Falk and van Wynsberghe, 2023). For instance, intelligent virtual assistants and customer service representatives can process an enormous number of questions, leaving more essential and creative tasks to people.

AI can also generate new occupations, but on the other hand, it can replace employees, and this might affect the manufacturing, retail, and transportation industries. For example, self-driving trucks, which are in the process of development, can lead to the disappearance of the need for truck drivers, or artificially intelligent cashier less stores can affect employees in the field of retail.

4.1.1.2 Skills and Education

In order to overcome the adverse effects of job displacement, there is a need to promote the programs of reskilling and upskilling (Akgun and Greenhow, 2022). To ensure that people can meet the demands of the AI revolutionized economy, governments, educational institutions, and businesses must work together to develop training programs. The fast advancement in AI requires a new approach towards training the systems in lifelong learning. It also requires that the workers should be motivated to acquire more skills so that they can be relevant in the market.

4.2 Inequality

AI technologies can widen the gap between the haves and the have-nots if the right measures are not taken (Roy, 2017). Accessibility of the advantages of AI and non-triviality of the disadvantages in the context of sustainable development of the states.

4.2.1 Digital Divide

AI innovation may worsen the digital divide since low-income households cannot afford the infrastructure, education, or resources needed to benefit. AI technologies must be accessible to all to avoid exclusion.

4.2.2 Economic Inequality

A few significant corporations and individuals who own and operate AI systems could profit from AI (Mujtaba and Mahapatra, 2019). This may increase inequality by concentrating wealth and income in a few hands. New and efficient AI technologies can raise the quality of high-skill occupations while decreasing demand for low-skill professions and lowering pay, polarizing the labour market.

4.3 Long-term Viability

Some of the most fundamental problems that require solutions for the AI technology to be sustainable include sustainability problems like energy use, environmental aspects, and ethical questions among others (Clarke & Whittlestone, 2022).

4.3.1 ENERGY CONSUMPTION

A majority of the AI models, with deep learning being the most popular, consume a lot of computational power and hence have high energy consumption. For example, the training of large language models like GPT-3 required a huge amount of electrical power (Khakurel et al., 2018).

4.3.2 ENVIRONMENTAL IMPACT

The development of AI systems and its distribution affects the environment since resources such as rare earth metals are used in AI (Wu et al., 2022a). This makes sustainable sourcing and recycling of the product a feasible approach to such effects. AI also has an opportunity to enhance the issue of resource utilization, monitoring of the changes in the environment, and support for conservation (van Wynsberghe, 2021). For instance, the sensors employing AI can monitor the pollution and regulate the energy usage in the smart grids.

4.4 Examples of Sustainable AI Applications

AI technologies should be used to increase sustainability in many fields by utilizing resources effectively and reducing impact on the environment, (Nishant, Kennedy and Corbett, 2020). Here are some examples of sustainable AI applications:

4.4.1 RENEWABLE ENERGY

Energy systems may be improved using AI in the generation of renewable energy resources and the optimization of systems (Chen et al., 2023).

4.4.2 Smart Grids

AI can predict the energy demand, optimize the supply and demand in smart grids, and include the renewable sources of energy. For energy loss and the imbalance of the electricity grid, these issues can be avoided (Wu et al., 2022b). AI can help to develop demand response programs which manage load according to the grid situation without fossil fuel peaking power stations and augmented carbon emissions.

4.4.2.1 Renewable Energy Forecasting

AI can improve renewable energy predictions by monitoring weather, production history, etc. Proper forecasting helps integrate renewable energy into the grid and reduce fossil fuel use (Wu and Shang, 2020). AI can predict equipment faults and schedule maintenance for renewable energy installations like turbines and solar panels to run effectively and last.

4.4.3 Agriculture

AI technologies can be beneficial to sustainable agriculture by improving efficiency, conserving resources, and lowering the negative effects on the environment (Zhu et al., 2018).

4.4.3.1 Precision Agriculture

Farmers may receive crop health, soil conditions, and moisture data from drones and sensors using AI. This reduces water, fertilizer, and pesticide waste and environmental damage by applying them properly to crops (Meyer et al., 2023).

AI can calculate planting schedules, crop yields, and other farm management aspects using satellite and weather data.

4.4.3.2 Sustainable Farming Practices

AI can help in efficient use of resources in agriculture by predicting water requirements, efficient use of water through irrigation systems and minimum use of chemicals (Kar, Choudhary and Singh, 2022). It can also keep track of the state of the soil and advise on how to preserve the fertility of the soil, reduce on soil erosion as well as support the soil biological diversity.

4.4.4 Urban Planning

AI has the potential of improving sustainable planning and management of urban infrastructures, resources, and the quality of life in the community (Walk et al., 2023).

4.4.4.1 Smart Cities

Figure 5: SUSTAINABLE AI APPLICATIONS

Sensors and cameras can assess traffic patterns in real time, lowering congestion and pollution. AI-developed traffic signal systems can learn from traffic (Marques et al., 2020).

Civil AI applications in public transportation include predicting service demand and analysing the most efficient routes to encourage car-free travel.

4.4.4.2 Energy Efficiency

AI can be used to conserve energy by regulating HVAC systems, lighting, and other devices according to people’s presence and other factors (Bracarense et al., 2022).

AI can help in the implementation of renewable energy sources into the urban energy systems thus helping the cities to decrease their emission levels and switch to sustainable energy solutions.

5 Conclusion

This report has discussed Applied AI in its various dimensions, namely, ethical, sustainability, and societal. The ethical principles are relevant to the present day’s AI systems including transparency, accountability, fairness, and robustness to reduce bias. AI’s energy requirements, effects on the environment and impact on society present problems that must be solved through technological advancements and policies. The use of AI in the healthcare sector, the financial industry, transportation, farming, and city management also has a great potential for improving productivity and efficiency.

Based on the findings, the following recommendations are proposed to foster responsible AI development and deployment:

Enhance Ethical Guidelines: Enhance codes of conduct and best practices to ensure that greater numbers of AI systems are developed and used ethically.

Promote Diversity and Inclusion: Promote the inclusion of diverse groups of people in the development of AI technologies to avoid reproduction of biases in the AI systems.

Invest in Sustainable Innovations: Financially support research and development regarding the energy efficiency of AI algorithms as well as the use of AI in renewable energy, agriculture, and city planning.

Collaborate for Regulation: Promote cooperation with other countries to set up the regulation that will allow further AI development but will also take into consideration the social and ethical impacts of such technologies on the society.

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Critical Evaluation of Applied AI: Ethical and Sustainability Perspectives (Individual Report)