The global artificial intelligence (AI) software market is expected to grow at a significant rate in the future, reaching a value of $126 billion by 2025. A report by Omdia predicted that AI will be a part of almost every new software by 2025.
While the integration of AI in all industries is an exciting prospect, there’s also a concern regarding ethics. The SaaS industry is in the early stages of AI adoption, with most contenders still unsure about the best implementation strategies.
The ethical integration of AI in SaaS products and services requires stakeholders to understand AI ethics and principles. In this guide, we look at AI ethics principles for SaaS businesses.
How AI is being used in the SaaS space
AI has a wide range of applications in SaaS, from marketing and customer segmentation to data analysis and resource allocation. Machine learning algorithms are beneficial in this regard since they allow companies to make decisions and predictions. Einstein Ai by Salesforce is an excellent example of this, as it assists sales reps in lead generation. Sales reps can also use the AI tool for personalized recommendations.
Besides data and predictive analysis, AI is also used for NLP (Natural Language Processing) technology. With NLP, computers can understand and interpret human language, allowing for the creation of virtual assistants and chatbots. Microsoft’s Cortana is an example of a virtual assistant that can give users reminders, send emails, schedule meetings, and more.
AI can also automate menial tasks, like report generation and data entry, for SaaS employees. It will give them more time to focus on demanding and strategic tasks.
Similarly, AI algorithms allow personalization. For example, SaaS marketing teams can use AI algorithms to provide personalized recommendations and messaging to increase conversion rates.
What is AI ethics?
According to IBM, AI ethics is a “multidisciplinary field” that deals with the optimization of AI’s benefits while lowering risks and “adverse outcomes.” Some common ethical issues in AI implementation are:
- Transparency
- Moral agency
- Accountability
- Misuse of technology
- Fairness
- Privacy and responsibility
As big data has now become mandatory in all organizations, more and more companies are focusing on data-driven decision-making. These decisions often require the use of AI algorithms to derive value from large data sets.
While this reduces manual work and makes decision-making more efficient, it also raises concerns about bias, privacy, transparency, and fairness.
AI’s ethical considerations in SaaS
Here are some considerations surrounding AI in SaaS:
- Privacy: AI and ML algorithms train on heaps of public data. Everyone’s beloved ChatGPT is also trained on hundreds of GB of internet data, including books, articles, and web pages. However, OpenAI’s revolutionary product is under fire from EU countries due to a violation of generative AI ethics. The tool is under investigation for breach of privacy. Other AI solutions can also see a similar fate. The risk is exceptionally high in industries that collect confidential information, such as healthcare and fintech.
- Bias: Since AI algorithms train on public data, human bias and social or historical inequities can creep into the equation. Thus, AI algorithms can pick up and magnify existing biases at scale, corrupting decisions and resulting in erroneous predictive analysis. The bias also leads to discriminatory outcomes, which can be problematic in situations like recruiting employees and loan processing.
- Accountability: It’s challenging to hold a company accountable for its AI decisions, especially in the SaaS space. An example is autonomous vehicles. Who is responsible for any harm caused by the AI-driven car? Is it the driver, the manufacturer, or the algorithm itself? The ambiguity makes accountability complicated.
Why is AI ethics important?
AI ethics is imperative in the SaaS industry for many reasons. For one, it ensures the development of AI algorithms safely. As long as there are no guidelines for AI development, companies will experience privacy and confidentiality-related issues.
Complying with AI ethics is also essential for trust maintenance. The data you use for your SaaS product or service comes from customers. So, it’s your responsibility to ensure the data is not exposed to any risk.
Following AI app ethics will also keep SaaS companies safe from legal repercussions of regulatory non-compliance. Governments around the world are becoming increasingly wary of AI and its impact on consumer data and privacy.
AI tools and algorithms found to be invasive or in breach of terms of service can face hefty fines. They may also be banned or blocked.
What are the 5 ethics in artificial intelligence?
The US Department of Defense (DOD) has listed five AI ethics principles that it will follow in its AI tools.
- Responsible: The personnel working with or developing AI will exercise appropriate care and judgment levels.
- Equitable: Necessary steps will be taken to reduce the bias in AI development.
- Traceable: The department will develop and deploy AI technologies in such a way that its decisions and results can be traced. These processes will have auditable and transparent methods, documentation, design procedures, and data sources.
- Reliable: The AI technologies will have well-defined and reliable use cases. Testing will be conducted to ensure the effectiveness of AI tools across the products’ life cycles.
- Governable: The department will design AI systems in a way that they can be deactivated or disengaged if they show unintended behavior.
Every SaaS business can use some variation of these principles to ensure their AI products or services stay ethical.
Forrester’s 5 AI ethics principles
Forrester has created a set of AI app ethics that SaaS and non-SaaS stakeholders can apply to their AI products and services.
- Fairness: The principle concerns the equitable use of AI. It requires organizations to develop and use AI without bias that could lead to discrimination of any kind. Fairness is paramount in generative AI ethics to ensure autonomous decision-making is not leading to prejudice.
- Transparency: Forrester has a report that discusses the accuracy/explainability trade-off in AI and ML. Developers can use it to ensure their products are transparent and understandable.
- Accountability: Someone has to be responsible for the damage or harm an AI framework causes. An integral part of AI ethics principles is to have some degree of accountability.
- Social benefit: Forrester also believes that AI should benefit society in one way or another. It should not be used to harm people in any form.
- Privacy and security: An AI system should be trained to safeguard confidential information. It should also comply with regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act).
UNESCO’s AI ethics principles
As we’ve already explained, organizations and governments have different views on AI ethics. UNESCO (United Nations Education, Science, and Culture Organization) has developed a set of ten principles in this regard. These include:
- Proportionality and doing no harm
- Safety and security
- Right to privacy and data protection
- Multi-stakeholder and adaptive governance and collaboration
- Responsibility and accountability
- Transparency and explainability
- Awareness and literacy
- Fairness and non-discrimination
- Sustainability
- Human oversight and determination
It’s easy to see that regardless of the organization behind them, the AI ethics principles essentially focus on protecting people’s rights, safety, and security. Like organizations, governments are also taking active steps to set guidelines for AI ethics.
For example, the Australian government has 8 AI ethics principles for businesses to follow. Stakeholders in the SaaS space should create organizational frameworks for AI ethics while keeping federal, industrial, and state laws in mind.
How can businesses incorporate AI in SaaS offerings ethically?
Before SaaS businesses decide to incorporate AI in their offerings, they should create a set of guidelines for developers to follow. You can take inspiration from existing AI ethics principles or draft your own.
It also helps to have a separate AI development team whose sole responsibility is maintaining AI ethics. The team should keep up with the latest trends, laws, and regulations surrounding AI ethics. It will help organizations avoid mistakes in their SaaS MVP development process.
Another important aspect of AI ethics is continuous testing. Before you release a feature for consumer use, test it thoroughly. For example, if you’ve created a chatbot to help customers, evaluate whether it provides accurate and reliable responses to customer queries.
Being open and transparent about AI use and implications is also essential. If you use AI for predictive analysis or profiling, ensure your customers understand what data you’re collecting and how it will be used.
If your product integrates or, in any way, related to mainstream companies like Amazon and Facebook, make sure it follows their ethical guidelines too. Partnership on AI is an industry organization that includes Amazon, Google, Facebook, and similar companies. These organizations have established ethical guidelines for accountability, fairness, and transparency.
Also, look out for SaaS industry-specific AI ethics initiatives, like Trustworthy Artificial Intelligence (TAI) groups by Cloud Security Alliance and Moz. These groups often have certification programs for AI products meeting their ethical standards. Getting these certifications is a great way to show your customers you are serious about AI ethics.
Taking an ethical approach to AI in SaaS
Developing software products is already an expensive and complicated process. Throw in AI, and you have an even greater set of complexities.
One thing is sure; AI and SaaS go well hand in hand. After all, what better way to make software more efficient and functional than giving it “intelligence” of its own?
However, SaaS businesses and development teams often overlook the importance of AI ethics. In some cases, they don’t understand the ethical implications of AI or are not equipped to deal with them.
Such businesses can rely on a software development solution, like VeryCreatives, to build a SaaS product with a proper AI ethics framework.
Interested in learning how we take an ethical approach to AI? Contact us today to learn more.
