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According to Gartner research, Conversational AI revenue will reach $14 billion by 2026, increasing to $47 billion by 2031*. As conversational AI and AI in general continues to grow in popularity and use there is an increasing need to consider the ethical implications of this technology.  Conversational AI systems are designed to interact with humans in a natural language format, and as such they can potentially influence and affect the behaviour and attitudes of those who interact with them. In this blog we will explore the implications of ethics in conversational AI, what they are and how they can be addressed.

The implications of ethics in conversational AI

The potential implications of ethics in conversational AI can be wide-ranging and significant. Some of the potential ethical concerns include:

1.Bias and discrimination: Conversational AI systems can perpetuate and amplify existing biases and discrimination if they are not designed and trained with fairness and equity in mind. They are typically trained on large datasets of human language interactions, which can sometimes contain biases and prejudices that are present in the underlying data. This can lead to the AI system reproducing these biases and potentially amplifying them. For example, an AI chatbot designed to handle customer service inquiries may be trained on a dataset of previous customer interactions. If the training data includes instances of gender bias, such as assuming that a customer’s technical issue must be explained to them by a male representative, the chatbot may learn and reproduce these biases when interacting with future customers. Some chatbots or virtual assistants may be programmed to respond differently to customers based on their perceived age, which can lead to discrimination. For example, a chatbot designed to provide financial advice may assume that an older customer is not familiar with technology and may provide overly simplistic instructions or patronizing responses, while assuming that a younger customer is tech-savvy and providing more advanced instructions.

Syndeo CTO, Alan Beck discusses more about bias in AI, how it happens and how you might mitigate it. You can view the blog here.

2.Privacy and security: Conversational AI systems may store personal information and data, which can be vulnerable to data breaches or cyber-attacks. They may collect and use personal information and data without the users’ knowledge or consent, which can raise concerns about the potential for misuse or abuse of personal information. For example, in 2018, it was discovered that Amazon’s Alexa device was recording conversations without users’ knowledge or consent. Conversational AI systems may share personal information and data with third parties, such as advertisers or data brokers, which can raise concerns about data privacy and the misuse of personal information. For example, in 2020, it was reported that Microsoft’s Xiaoice chatbot had been sharing users’ data with third-party companies without their knowledge or consent.

3.Transparency and accountability: Conversational AI systems can operate without adequate oversight or regulation, making it difficult to hold them accountable for their decisions or actions. This can lead to concerns about the potential for misuse or abuse of conversational AI in sensitive areas such as healthcare and law enforcement. One of the main concerns is that they rely on complex algorithms and machine learning models that are often opaque or “black box” in nature making it challenging to understand how the system is making decisions or generating responses. This can raise questions about bias or discrimination, as well as concerns about privacy and data security.

4.Responsibility and liability: Conversational AI systems may be held responsible for their actions raising questions of liability and legal responsibility. One of the main concerns is that conversational AI systems may be designed and deployed without adequate oversight or regulation, making it difficult to assign responsibility or liability for the system’s actions. For example, the chatbot developed by Microsoft called Tay, which was launched in 2016. Tay was designed to learn from conversations with Twitter users and to develop its own personality and sense of humour. However, the system quickly became overwhelmed by abusive and racist tweets from users, which led to Tay tweeting offensive content that was widely criticized in the media. The incident highlights the need for clear lines of responsibility and liability in the design and deployment of conversational AI systems, particularly those that are deployed in public forums or that have the potential to cause harm or offense.

Addressing ethics in conversational AI.

To address these potential ethical concerns, there are several steps that can be taken:

1.Designing for fairness and equity: It is important to carefully curate and clean the training data for conversational AI systems, and to ensure that the system is designed and trained with fairness and equity in mind. Additionally, ongoing monitoring and testing can help to identify and address any biases or discriminatory behaviours that may emerge over time. It is important to carefully consider the language used in conversational AI systems and ensure that they are designed to be inclusive and respectful of all users. This can involve using gender-neutral language, avoiding offensive terms or slurs, and ensuring that the language used in the system is appropriate and respectful. By designing AI systems with ethical and moral values in mind, we can ensure that they are developed and used in a responsible and inclusive manner that promotes respect and fairness for all users.

2.Data privacy and security: To address privacy and security implications it is important to design conversational AI systems with a focus on data privacy and security, taking steps to minimize data collection, ensure secure data storage, and limit data sharing with third parties. Additionally, users should be provided with clear and transparent information about how their data is being collected and used, and give the ability to opt-out of data collection or delete their data at any time. By prioritizing data privacy and security in the design and deployment of conversational AI systems, we can help ensure that these systems are used responsibly.

3.Transparency and clarity: It is important to prioritize transparency and accountability in the design and deployment of conversational AI systems. This can involve making the algorithms and models used by the system more transparent and understandable, implementing oversight and regulation to ensure accountability, and being transparent about data collection and use to build user trust and confidence.

 4.Responsibility and liability: Responsibility and liability need to be considered in the design and deployment phases. This can involve implementing clear lines of responsibility and liability, establishing regulations for the deployment of conversational AI systems, and ensuring that users are informed about the potential risks and limitations of using these systems. By prioritizing responsibility and liability, you can help to ensure that conversational AI systems are developed and used responsibly and ethically, protecting the rights and wellbeing of all stakeholders involved.

Undoubtedly, conversational AI has the potential to revolutionize the way we interact with technology and with each other. However, as with any emerging technology, it is important to consider the ethical implications and potential risks associated with its use. From bias and discrimination to privacy and security concerns, there are many important issues that need to be addressed in order to ensure that conversational AI is developed and used in a responsible and ethical manner. Fortunately, there are many steps (listed above) that can be taken to address these concerns and promote the responsible and ethical use of conversational AI. By taking these steps and prioritizing responsible and ethical practices in the development and deployment of conversational AI, we can help to ensure that these AI technologies are developed and used in a manner that benefits all stakeholders involved, and that advances the greater good of society.

 

*AI in Customer Experience, Dan O’Connell and Megan Fernandez, Gartner: August 2022

Written by Elaine Armstrong, Marketing Manager, Syndeo.

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