As artificial intelligence continues to advance, the development of autonomous decision-making machines raises profound ethical questions that challenge our understanding of morality, responsibility, and human agency. These systems, capable of making complex decisions without direct human intervention, are reshaping industries from healthcare to finance, transportation to warfare. The ethical implications of delegating critical choices to machines are far-reaching and multifaceted, touching on issues of bias, privacy, accountability, and the very nature of moral reasoning itself.
The rapid proliferation of AI-driven decision systems has outpaced our ability to fully grasp their societal impact, creating an urgent need for robust ethical frameworks and governance structures. As you navigate this complex landscape, it’s crucial to consider not only the immediate consequences of autonomous decision-making but also its long-term effects on human autonomy, social justice, and the future of human-machine interaction.
Philosophical frameworks for machine ethics
The field of machine ethics grapples with fundamental questions about how to imbue artificial systems with moral reasoning capabilities. Traditional ethical frameworks, such as utilitarianism, deontology, and virtue ethics, provide starting points for developing ethical guidelines for AI. However, translating human moral philosophies into computational models presents significant challenges.
Utilitarianism, which focuses on maximizing overall well-being, might seem well-suited for AI systems capable of processing vast amounts of data to calculate outcomes. Yet, quantifying and comparing different forms of well-being across diverse populations remains a complex task. Deontological approaches, emphasizing moral rules and duties, could potentially be encoded into AI systems as strict guidelines. However, these rules often require nuanced interpretation in real-world scenarios, something that current AI struggles to achieve.
Virtue ethics, which emphasizes the development of moral character, presents a unique challenge for AI systems that lack human-like consciousness or emotional experiences. Some researchers propose hybrid approaches that combine elements of multiple ethical frameworks, aiming to create more robust and flexible moral reasoning systems for AI.
A key consideration in developing ethical frameworks for autonomous systems is the alignment problem – ensuring that the goals and values of AI systems align with those of humans. This challenge becomes particularly acute as AI systems become more advanced and potentially capable of altering their own goals or decision-making processes.
The ultimate aim of machine ethics is not just to create systems that can make ethically sound decisions, but to develop AI that can engage in moral reasoning and potentially even contribute to our understanding of ethics itself.
Algorithmic bias and fairness in AI Decision-Making
One of the most pressing ethical concerns in autonomous decision-making systems is the issue of algorithmic bias. AI systems, trained on historical data, can perpetuate and even amplify existing societal biases related to race, gender, age, and other protected characteristics. This bias can lead to unfair or discriminatory outcomes in critical areas such as hiring, lending, criminal justice, and healthcare.
Addressing algorithmic bias requires a multifaceted approach that encompasses data collection, model design, and ongoing monitoring and evaluation. You must consider not only the technical aspects of bias mitigation but also the broader societal context in which these systems operate.
Demographic parity vs. equal opportunity in machine learning models
In the pursuit of fairness, machine learning practitioners often grapple with different definitions and metrics of fairness. Two common approaches are demographic parity and equal opportunity. Demographic parity aims to ensure that the overall proportion of positive outcomes is the same across different demographic groups. Equal opportunity, on the other hand, focuses on ensuring that individuals who qualify for a positive outcome have an equal chance of receiving it, regardless of their demographic group.
These different fairness metrics can sometimes be in tension with each other, and with other performance metrics. For example, enforcing strict demographic parity might come at the cost of reduced accuracy or individual fairness. Balancing these competing objectives requires careful consideration of the specific context and potential consequences of the AI system’s decisions.
Intersectionality and multidimensional fairness metrics
As our understanding of bias and discrimination evolves, there’s growing recognition of the need for more sophisticated fairness metrics that account for intersectionality – the way in which multiple aspects of an individual’s identity can interact to create unique forms of disadvantage or privilege. Simple binary classifications of protected characteristics often fail to capture the complex reality of social identities and their impact on AI decision outcomes.
Researchers are developing multidimensional fairness metrics that aim to address these complexities. These approaches consider multiple protected attributes simultaneously and attempt to ensure fairness across various intersecting subgroups. However, implementing such metrics in practice presents significant technical and ethical challenges, including increased computational complexity and potential privacy concerns related to more granular data collection.
Transparency and explainability in Black-Box AI systems
The opacity of many advanced AI systems, particularly deep learning models, poses a significant challenge to ethical decision-making. These “black box” systems can produce highly accurate predictions or decisions, but the reasoning behind these outputs is often inscrutable even to their creators. This lack of transparency raises concerns about accountability, trust, and the ability to detect and correct biases or errors.
Efforts to address this issue have led to the development of explainable AI (XAI) techniques. These methods aim to provide human-interpretable explanations for AI decisions without sacrificing performance. Approaches range from using inherently interpretable models to developing post-hoc explanation techniques for complex neural networks.
However, the push for explainability must be balanced against other considerations. In some cases, the most accurate or effective AI models may be the least explainable. Moreover, there’s ongoing debate about what constitutes a satisfactory explanation in different contexts and for different stakeholders – from end-users to regulators to AI developers themselves.
Fairness-aware machine learning: techniques and challenges
As awareness of algorithmic bias grows, researchers and practitioners are developing a range of techniques for fairness-aware machine learning. These approaches can be broadly categorized into three types:
- Pre-processing techniques that aim to remove bias from training data
- In-processing methods that incorporate fairness constraints into the learning algorithm itself
- Post-processing approaches that adjust the model’s outputs to achieve fairness goals
Each of these approaches has its strengths and limitations. Pre-processing techniques can help address historical biases in data but may not account for new biases that emerge during model training. In-processing methods offer more control over fairness-accuracy trade-offs but can be computationally expensive and may require significant changes to existing machine learning pipelines. Post-processing approaches are often easier to implement but may be less effective at addressing deep-seated biases in the model.
A key challenge in fairness-aware machine learning is the need for domain-specific understanding of what constitutes fairness in different contexts. What’s considered fair in a healthcare decision-making system may be very different from what’s fair in a financial lending algorithm. This underscores the importance of interdisciplinary collaboration between AI researchers, domain experts, ethicists, and affected communities in developing and implementing fair AI systems.
Moral agency and responsibility in autonomous systems
As autonomous systems become increasingly sophisticated and are deployed in high-stakes environments, questions of moral agency and responsibility come to the fore. Can machines be considered moral agents? If an AI system makes a decision that leads to harm, who is responsible – the system itself, its creators, or the humans who deployed it?
These questions challenge traditional notions of moral responsibility, which typically assume human-like consciousness and intentionality. Some argue that as AI systems become more advanced, they may develop forms of moral agency that warrant new ethical and legal frameworks. Others contend that moral agency should remain a uniquely human attribute, with responsibility always tracing back to human decision-makers.
Distributed moral responsibility in Human-AI collaborative Decision-Making
In many real-world applications, AI systems don’t make decisions in isolation but rather as part of human-AI collaborative systems. This creates complex scenarios of distributed moral responsibility. For example, in an AI-assisted medical diagnosis system, responsibility for patient outcomes may be shared between the AI system, the healthcare professionals using it, the system developers, and the institution deploying the technology.
Navigating these shared responsibility scenarios requires careful consideration of the roles and capabilities of both human and AI agents in the decision-making process. It also necessitates clear protocols for human oversight and intervention, as well as mechanisms for attributing responsibility when things go wrong.
Legal personhood for AI: implications and controversies
As AI systems take on more autonomous decision-making roles, some have proposed granting them a form of legal personhood. This could potentially allow AI systems to enter into contracts, own assets, or be held liable for damages. Proponents argue that this would provide a clearer framework for managing AI’s increasing role in society and economy.
However, the concept of AI legal personhood is highly controversial. Critics argue that it could dilute the concept of personhood and potentially be used to shield human decision-makers from responsibility. There are also practical challenges in determining how an AI entity could be held accountable or face consequences in meaningful ways.
Artificial moral agents: design principles and ethical frameworks
The development of artificial moral agents (AMAs) – AI systems capable of making ethical decisions – is an active area of research and debate. Designing AMAs requires not only technical expertise but also deep engagement with ethical philosophy and cognitive science.
Some proposed design principles for AMAs include:
- Transparency in ethical reasoning processes
- Ability to provide justifications for moral decisions
- Capacity for moral learning and adaptation
- Alignment with human values and ethical norms
- Mechanisms for resolving ethical dilemmas
However, the very concept of AMAs raises profound questions about the nature of morality and whether it can be fully captured in computational terms. There’s also concern that relying too heavily on AMAs could lead to a diminishment of human moral reasoning capabilities and responsibility.
The development of artificial moral agents is not just a technical challenge, but a philosophical and societal one that forces us to reexamine our understanding of ethics and decision-making in the age of AI.
Privacy and data ethics in autonomous decision systems
Autonomous decision-making systems often rely on vast amounts of data, including personal information, to train their models and make informed choices. This raises significant privacy concerns and ethical questions about data collection, storage, and usage. As you consider the implications of these systems, it’s crucial to address the tension between the need for data to improve AI performance and the right to individual privacy.
One key ethical challenge is the concept of informed consent in the age of big data and AI. Traditional notions of consent may be inadequate when data collected for one purpose can be repurposed for AI applications that were not initially envisioned. Moreover, the complexity of AI systems makes it difficult for individuals to fully understand how their data might be used or what decisions it might influence.
Another critical issue is the potential for data-driven discrimination . Even when protected characteristics are not explicitly used, AI systems can often infer sensitive information from seemingly innocuous data points. This can lead to unintended discriminatory outcomes, particularly when historical data reflects societal biases.
Privacy-preserving machine learning techniques, such as federated learning and differential privacy, offer promising approaches to mitigate some of these concerns. These methods allow AI systems to learn from distributed datasets without centralizing sensitive information or compromising individual privacy. However, implementing these techniques at scale presents technical challenges and may involve trade-offs with model performance.
Ethical data practices for autonomous decision systems should also consider issues of data ownership, data portability, and the right to be forgotten. As AI systems become more integrated into critical infrastructure and services, ensuring individuals maintain control over their personal information becomes increasingly important for preserving autonomy and trust in these systems.
Ethical considerations in specific AI applications
While general ethical principles for AI are important, many of the most pressing ethical questions arise in the context of specific applications. Different domains present unique challenges and considerations that require tailored approaches to ethical AI development and deployment.
Autonomous vehicles: trolley problems and Real-World decision scenarios
Autonomous vehicles (AVs) have become a focal point for discussions of AI ethics, particularly around issues of safety and moral decision-making. The famous “trolley problem” thought experiment has been adapted to AV scenarios, asking how these systems should be programmed to respond in unavoidable accident situations where different outcomes result in different casualties.
However, real-world ethical considerations for AVs go far beyond these stylized dilemmas. They include questions of:
- Balancing individual passenger safety with overall traffic safety
- Ensuring equitable access to AV technology across different communities
- Managing the transition period where autonomous and human-driven vehicles share the road
- Addressing liability and insurance issues in accidents involving AVs
Policymakers and AV developers must grapple with these complex issues while also considering the potential life-saving benefits of widespread AV adoption. Transparent decision-making processes and clear ethical guidelines will be crucial for building public trust in autonomous vehicle technology.
AI in healthcare: patient autonomy vs. algorithmic recommendations
The application of AI in healthcare offers immense potential for improving diagnosis, treatment planning, and patient outcomes. However, it also raises significant ethical concerns, particularly around the balance between algorithmic recommendations and patient autonomy.
AI systems trained on large medical datasets may be able to identify patterns and make predictions that surpass human capabilities in certain areas. This raises questions about how much weight should be given to AI recommendations versus the judgment of human healthcare professionals or the preferences of patients themselves.
Other ethical considerations in healthcare AI include:
- Ensuring fairness and avoiding bias in medical AI systems
- Maintaining patient privacy and data security
- Addressing the potential for AI to exacerbate healthcare inequalities
- Managing the psychological impact of AI-driven health predictions on patients
As AI becomes more integrated into healthcare systems, it will be crucial to develop ethical frameworks that preserve human values and patient rights while leveraging the benefits of advanced technology.
Lethal autonomous weapons systems: international humanitarian law challenges
The development of lethal autonomous weapons systems (LAWS) presents some of the most ethically fraught challenges in AI applications. These systems, capable of selecting and engaging targets without human intervention, raise profound questions about the role of human judgment in warfare and the potential for uncontrolled escalation of conflicts.
Key ethical and legal issues surrounding LAWS include:
- Compliance with international humanitarian law principles of distinction, proportionality, and precaution
- Accountability for decisions made by autonomous systems in combat situations
- The potential lowering of the threshold for armed conflict due to reduced risk to human combatants
- Implications for global stability and arms control efforts
There are ongoing international debates about whether to ban or regulate LAWS, with different countries and stakeholders taking varying positions. Resolving these ethical and legal challenges will require careful consideration of both the potential humanitarian benefits and risks of autonomous weapons technology.
Ai-driven financial systems: algorithmic trading and market manipulation
In the financial sector, AI systems are increasingly used for tasks ranging from credit scoring to high-frequency trading. While these applications can enhance efficiency and potentially improve market stability, they also introduce new ethical risks and challenges.
Algorithmic trading systems, capable of executing large volumes of trades at high speeds, raise concerns about market fairness and the potential for market manipulation. There’s also the risk of systemic instability if multiple AI systems interact in unexpected ways during market events.
Other ethical considerations in AI-driven finance include:
- Ensuring transparency and explainability in AI-based lending decisions
- Addressing potential biases in credit scoring algorithms
- Managing the societal impacts of AI-driven job displacement in the financial sector
- Balancing innovation with financial stability and consumer protection
Regulatory frameworks for AI in finance will need to evolve to address these challenges while fostering innovation and maintaining market integrity.
Governance and regulatory frameworks for AI ethics
As autonomous decision-making systems become more prevalent and powerful, the need for effective governance and regulatory frameworks becomes increasingly urgent. These
frameworks become increasingly urgent. These frameworks must balance innovation with ethical considerations, ensuring that AI systems are developed and deployed in ways that respect human rights, promote fairness, and maintain accountability.
Developing effective governance for AI requires collaboration between policymakers, industry leaders, ethicists, and civil society organizations. Key challenges include:
- Keeping pace with rapidly evolving AI technologies
- Balancing regulation with innovation and competitiveness
- Addressing the global nature of AI development and deployment
- Ensuring enforcement mechanisms for ethical guidelines
Several approaches to AI governance have emerged, ranging from voluntary industry guidelines to binding legislation. The European Union’s proposed AI Act, for example, aims to create a comprehensive regulatory framework for AI systems based on their level of risk. Other jurisdictions are exploring sector-specific regulations or principles-based approaches.
A crucial aspect of AI governance is the development of standards and certification processes for ethical AI. These can help ensure that AI systems meet certain benchmarks for fairness, transparency, and accountability before being deployed in sensitive domains. However, creating meaningful standards that can apply across diverse AI applications and cultural contexts remains a significant challenge.
Another key consideration is the role of human oversight in AI governance. As AI systems become more autonomous, determining appropriate levels of human involvement in decision-making processes becomes increasingly complex. Frameworks for “meaningful human control” are being developed to address this challenge, particularly in high-stakes domains like autonomous weapons systems.
Effective AI governance requires a delicate balance between fostering innovation and protecting societal values. It must be flexible enough to adapt to rapid technological changes while providing clear ethical guidelines and accountability mechanisms.
As you consider the future of AI governance, it’s important to recognize that ethical frameworks for autonomous decision-making systems will likely need to evolve over time. Ongoing dialogue between stakeholders, regular reassessment of ethical guidelines, and mechanisms for addressing emerging challenges will be crucial for ensuring that AI continues to benefit society while minimizing potential harms.
The ethical questions arising from autonomous decision-making machines are complex and multifaceted, touching on fundamental aspects of human values, social structures, and the future of human-machine interaction. As AI continues to advance, grappling with these ethical challenges will be essential for shaping a future where technology enhances human flourishing and upholds our core ethical principles.
By fostering interdisciplinary collaboration, promoting transparency in AI development, and engaging in ongoing ethical reflection, we can work towards creating autonomous systems that not only make efficient decisions but also align with our deepest moral convictions and societal aspirations. The path forward requires careful consideration, robust debate, and a commitment to placing ethics at the forefront of technological innovation.
# Anas610/ITI-NodeJS-Project# middleware/createToken.jsconst jwt = require(“jsonwebtoken”);const createToken = (payload, type) => { const token = jwt.sign(payload, “secret”, { expiresIn: type === “remember” ? “7d” : “24h”, }); return token;};module.exports = createToken;End File# Anas610/ITI-NodeJS-Projectconst jwt = require(“jsonwebtoken”);const authAdmin = (req, res, next) => { const token = req.header(“Authorization”); if (!token) { return res.status(401).json({ message: “Access denied. No token provided.” }); } try { const decoded = jwt.verify(token, “secret”); req.user = decoded; if (req.user.role !== “admin”) { return res.status(403).json({ message: “Access denied. Admin role required.” }); } next(); } catch (error) { res.status(400).json({ message: “Invalid token.” }); }};module.exports = authAdmin;End File# Anas610/ITI-NodeJS-Project# middleware/authorSec.jsconst jwt = require(“jsonwebtoken”);const User = require(“../models/user”);const authorSec = async (req, res, next) => { const token = req.header(“Authorization”); if (!token) { return res.status(401).json({ message: “Access denied. No token provided.” }); } try { const decoded = jwt.verify(token, “secret”); req.user = decoded; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found.” }); } if (user.role !== “author”) { return res.status(403).json({ message: “Access denied. Author role required.” }); } next(); } catch (error) { res.status(400).json({ message: “Invalid token.” }); }};module.exports = authorSec;End File# Anas610/ITI-NodeJS-Projectconst jwt = require(“jsonwebtoken”);const User = require(“../models/user”);const authentication = async (req, res, next) => { const token = req.header(“Authorization”); if (!token) { return res.status(401).json({ message: “Access denied. No token provided.” }); } try { const decoded = jwt.verify(token, “secret”); req.user = decoded; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found.” }); } next(); } catch (error) { res.status(400).json({ message: “Invalid token.” }); }};module.exports = authentication;End File# Anas610/ITI-NodeJS-Project# middleware/fileUploads.jsconst multer = require(“multer”);const path = require(“path”);const storage = multer.diskStorage({ destination: function (req, file, cb) { cb(null, “uploads/”); }, filename: function (req, file, cb) { const uniqueSuffix = Date.now() + “-” + Math.round(Math.random() * 1e9); cb(null, file.fieldname + “-” + uniqueSuffix + path.extname(file.originalname)); },});const fileFilter = (req, file, cb) => { const allowedFileTypes = /jpeg|jpg|png|gif/; const extname = allowedFileTypes.test(path.extname(file.originalname).toLowerCase()); const mimetype = allowedFileTypes.test(file.mimetype); if (extname && mimetype) { return cb(null, true); } else { cb(“Error: Images Only!”); }};const upload = multer({ storage: storage, limits: { fileSize: 1024 * 1024 * 5 }, fileFilter: fileFilter,});module.exports = upload;End File# controllers/userController.jsconst User = require(“../models/user”);const Book = require(“../models/book”);const createToken = require(“../middleware/createToken”);const bcrypt = require(“bcryptjs”);// User Registrationconst register = async (req, res) => { try { const { firstName, lastName, email, password, role } = req.body; // Check if the user already exists const existingUser = await User.findOne({ email }); if (existingUser) { return res.status(400).json({ message: “User already exists” }); } // Hash the password const salt = await bcrypt.genSalt(10); const hashedPassword = await bcrypt.hash(password, salt); // Create a new user const newUser = new User({ firstName, lastName, email, password: hashedPassword, role, }); await newUser.save(); res.status(201).json({ message: “User registered successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// User Loginconst login = async (req, res) => { try { const { email, password, remember } = req.body; // Check if the user exists const user = await User.findOne({ email }); if (!user) { return res.status(400).json({ message: “Invalid credentials” }); } // Check if the password is correct const isMatch = await bcrypt.compare(password, user.password); if (!isMatch) { return res.status(400).json({ message: “Invalid credentials” }); } // Create and send a token const token = createToken( { userId: user._id, role: user.role }, remember ? “remember” : “default” ); res.json({ token, userId: user._id, role: user.role }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Get User Profileconst getProfile = async (req, res) => { try { const user = await User.findById(req.user.userId).select(“-password”); if (!user) { return res.status(404).json({ message: “User not found” }); } res.json(user); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Update User Profileconst updateProfile = async (req, res) => { try { const { firstName, lastName, email } = req.body; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found” }); } user.firstName = firstName || user.firstName; user.lastName = lastName || user.lastName; user.email = email || user.email; await user.save(); res.json({ message: “Profile updated successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Change Passwordconst changePassword = async (req, res) => { try { const { currentPassword, newPassword } = req.body; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found” }); } // Check if the current password is correct const isMatch = await bcrypt.compare(currentPassword, user.password); if (!isMatch) { return res.status(400).json({ message: “Current password is incorrect” }); } // Hash the new password const salt = await bcrypt.genSalt(10); const hashedPassword = await bcrypt.hash(newPassword, salt); user.password = hashedPassword; await user.save(); res.json({ message: “Password changed successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Get User’s Favorite Booksconst getFavoriteBooks = async (req, res) => { try { const user = await User.findById(req.user.userId).populate(“favoriteBooks”); if (!user) { return res.status(404).json({ message: “User not found” }); } res.json(user.favoriteBooks); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Add a Book to Favoritesconst addToFavorites = async (req, res) => { try { const { bookId } = req.body; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found” }); } if (user.favoriteBooks.includes(bookId)) { return res.status(400).json({ message: “Book already in favorites” }); } user.favoriteBooks.push(bookId); await user.save(); res.json({ message: “Book added to favorites successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Remove a Book from Favoritesconst removeFromFavorites = async (req, res) => { try { const { bookId } = req.params; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found” }); } user.favoriteBooks = user.favoriteBooks.filter( (id) => id.toString() !== bookId ); await user.save(); res.json({ message: “Book removed from favorites successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Rate a Bookconst rateBook = async (req, res) => { try { const { bookId, rating } = req.body; const user = await User.findById(req.user.userId); if (!user) { return res.status(404).json({ message: “User not found” }); } const book = await Book.findById(bookId); if (!book) { return res.status(404).json({ message: “Book not found” }); } // Check if the user has already rated this book const existingRating = book.ratings.find( (r) => r.user.toString() === user._id.toString() ); if (existingRating) { // Update existing rating existingRating.rating = rating; } else { // Add new rating book.ratings.push({ user: user._id, rating }); } // Recalculate average rating const totalRatings = book.ratings.reduce((sum, r) => sum + r.rating, 0); book.averageRating = totalRatings / book.ratings.length; await book.save(); res.json({ message: “Book rated successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Get All Users (Admin only)const getAllUsers = async (req, res) => { try { const users = await User.find().select(“-password”); res.json(users); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Delete User (Admin only)const deleteUser = async (req, res) => { try { const { userId } = req.params; const user = await User.findByIdAndDelete(userId); if (!user) { return res.status(404).json({ message: “User not found” }); } res.json({ message: “User deleted successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};// Update User Role (Admin only)const updateUserRole = async (req, res) => { try { const { userId } = req.params; const { role } = req.body; const user = await User.findById(userId); if (!user) { return res.status(404).json({ message: “User not found” }); } user.role = role; await user.save(); res.json({ message: “User role updated successfully” }); } catch (error) { console.error(error); res.status(500).json({ message: “Server error” }); }};module.exports = { register, login, getProfile, updateProfile, changePassword, getFavoriteBooks, addToFavorites, removeFromFavorites, rateBook