In our modern globalized world it’s difficult to find a person who has never heard of machine translation (MT) or utilized it to some extent. From our computers and mobile devices to the online translation services we use, machine translation has become an integral part of how we communicate, share information, and navigate through modern society.
Whether we’re making international calls, accessing subtitles for foreign movies, YoutTube videos or quickly translating texts for professional or personal needs, machine translation has become an important and hands-on technology for everyone.
As machine translation continues to make progress, its adoption is becoming more widespread in many different areas: from website localization to medicine, customs or law services. However, the application of the technology also involves some ethical considerations that users and organizations should be aware of.
In the following article we will explore key ethical concerns surrounding machine translation and learn how to make it secure.
Data Privacy Concern: should you be worried?
The most important issue concerning the machine translation process is the possibility of data breaches. Frequent use of free machine translation services can lead to the exposure of sensitive information, especially of corporate character.
The infamous “Statoil Incident” serves as an example of the risks caused by the utilization of free online translation platform Translate.com to handle sensitive business documents and communications of the prominent Norwegian oil company Statoil.
This incident served as a wake-up call for organizations and businesses to carefully evaluate their machine translation practices and prioritize the use of secure, robust machine translation solutions that offer data protection and privacy features.
Biased Machine Translation: what can go wrong?
Machine translation models can sometimes reflect and reproduce biases, such as gender, cultural, or linguistic ones. This is an issue that needs to be addressed properly by developers, researchers or by anyone who utilizes machine translation solutions quite often.
MT can sometimes show gender stereotyping, such as translating gender-neutral terms in the source language into gendered terms in the target language that reinforce traditional gender roles. For example, translating the word “nurse” to a female pronoun even when the original text did not specify the gender.
Cultural bias occurs when machine translation models are trained on data from a limited set of sources. It can lead to the wrong translations that favor certain cultural norms or fail to properly convey cultural nuances. For example, a Chinese idiom about “losing face” is usually translated as a literal loss of facial features so that the underlying cultural significance is completely lost.
The languages and linguistic structures represented in the training data for MT models can also introduce biases. MT models trained mainly on European languages may struggle with accurate translations to and from non-European languages that have very different grammatical structures and linguistic features.
For example, European languages often have grammatical gender systems, where nouns are classified as masculine, feminine, or neuter. Many non-European languages, such as Chinese, Japanese, and Turkish, do not have this concept of grammatical gender. MT models trained on European language data may incorrectly assign gender to translations in languages that do not have this linguistic feature.
A user’s guide: what can be done?
One of the most effective approaches to deal with biases and guarantee data privacy in machine translation is the utilization of reliable and secure machine translation services. Here are some key considerations for developers and regular users on this matter:
- Choose machine translation services that prioritize data security and user privacy. These services should use encryption, secure data handling, and strict access controls to protect sensitive information during the translation process.
- Search for MT providers that are transparent about their data sources, model architectures, and bias mitigation strategies. Look for services that allow for independent audits and evaluations to ensure accountability and reduce the risk of erroneous translations.
- Utilize machine translation services that offer the ability to customize models or use domain-specific models. Consider solutions where the MT models and infrastructure are hosted and managed within the organization’s own IT environment, rather than relying on a cloud-based service.
- Opt for services that integrate seamlessly with human translation workflows, allowing for review, editing, and feedback. This enables subject matter experts and linguists to identify and correct biases in the automatic translations.
- Prioritize AI machine translation services that have robust support for a diverse range of languages, including non-European languages, such as Lingvanex, Google Translate, Microsoft, etc. This helps reduce the bias that can arise from training models primarily on European language data.
- Choose MT providers that actively work on improving their models, incorporating new data sources, and adhere to strict data protection regulations like the GDPR. Regular updates and improvements can help keep the machine translation services up-to-date and reduce the impact of biases over time.
Want to keep your private data secure? By following these steps you can ensure that your machine translation experiences are both secure and unbiased, allowing you to benefit from accurate and reliable translations while maintaining the integrity of your data and the inclusiveness of your content.
Let’s wrap it up
While machine translation has become an integral part of modern communication, users and organizations need to be aware of the ethical concerns surrounding data privacy and biases in the technology.
By choosing secure, transparent, and customizable machine translation services, and integrating human review and feedback, individuals and businesses can ensure that their machine translation experiences are both secure and unbiased. Careful consideration of these factors can help maximize the benefits of machine translation while minimizing the risks.
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