Although women make up around 35% of the workforce at SAP, when it comes to shaping AI, it seems they punch above their weight.
Here, five SAP employees share their roles, motivations, and advice for anyone looking to get started in AI. The women – Khawla Mallat, Camila Lombana Diaz, Xin Chen, Nadine Hoffmann and Puntis Palazzolo – span four countries and three fields: data science engineering, product management and AI ethics.
Data Science Engineering
Dr Khawla Mallat, Quantum Security and Exploration team, SAP France
“Be ready to face challenges at any time,” says Mallat, if you want to pursue a career in AI.
Unlike most data scientists at SAP, Mallat does not work directly on the product but is firmly rooted in researching and solving “some of the technical challenges around AI, namely fairness, explainability, confidentiality and security.
Before joining SAP two years ago, Mallat encountered the unintentional biases of facial analysis systems. Certain demographic groups, Mallat explains, were inadvertently omitted or underrepresented in the underlying datasets, leading to gaps in facial analysis capabilities. Such cases highlight the broader problem of bias in other applications of AI. In fields such as human resources, she continues, removing personal information from data sets may appear to resolve bias issues, but AI can still infer this information. This leads to potentially biased results and the lack of explainability of AI models makes it difficult to identify these biases. Allowing data sets like this to take root and develop into powerful data models without scrutiny will only amplify inherent bias or discrimination.
Today, Mallat’s passion for fighting such injustices aligns well with his role in identifying the risks inherent in AI, educating teams about them, and defining technology solutions to mitigate them.
“We need to take an interdisciplinary approach to AI, with experts in ethics, legal compliance and the field, for example, and abstract from the role of data scientists to succeed,” says Mallat.
“I love working in AI,” she continues. “Everything is moving at an incredible pace, so if you want to work in AI, you have to have a certain thirst for knowledge. And whatever your role, you need to take AI ethics – regulations and regulatory frameworks – seriously because these have huge implications not only for SAP but also for individuals and companies in general.
AI Ethics
Camila Lombana Diaz, Head of AI, Germany
“AI is a mirror of our capabilities as human beings. And the biggest responsibility of those working in AI is: what do we want to see in this mirror? says Lombana Diaz, AI ethics research expert on the AI Ethics/Responsible AI team located in the SAP Business AI Growth Area.
Lombana Diaz’s responsibilities include maturing and enforcing SAP AI’s global ethics policy, creating and delivering enablement content, defining AI personas and processes, and providing guidance to make responsible AI an operational reality for development, as illustrated in the SAP AI Ethics Handbook.
When she joined SAP eight years ago, first as a UX designer and then a strategic designer, machine learning and AI were central topics. But it became increasingly clear to him that “understanding the human implications of AI for responsible and ethical AI requires a human-centered perspective.”
Even though SAP is committed to ethical AI development – developers must now complete AI ethical assessment tasks and a steering committee reviews all high-risk use cases – Lombana Diaz emphasizes the need to remain focused on the inherent risks and unintended harm that AI can cause. here. Part of his role is to continually evaluate the technology, identify risks and limitations and communicate them to different teams.
As AI continues to rapidly evolve, so do roles. Lombana Diaz is passionate about seeing AI beyond the confines of a technology-centric perspective. “AI is now a ubiquitous technology shaping our daily lives; therefore, we need people working in the field that challenge AI technology to be community-focused. AI ethics is a space for experimental, open, curious, collaborative and human-centered individuals” and, she concludes, “now is the time to jump into AI and build a career . NOW because, unlike technology, the business of AI, its legal and ethical aspects, are still being shaped. »
Data Science Engineering
Dr. Xin Chen, SAP HANA Machine Learning, China
“I have always enjoyed working at SAP since I arrived nine years ago. I love the work environment and the colleagues here and I really want to encourage others to join us here in the AI field,” says Chen, a data science researcher on the SAP HANA Machine Learning team .
The team is working on a toolkit providing different types of machine learning algorithms for regression, classification, clustering, etc. for the SAP HANA predictive analytics library.
Part of Chen’s role is to study research papers on the latest machine learning algorithms and, with the team, decide which algorithms would be beneficial to clients. Once the machine learning algorithms are implemented, Chen and the team evaluate customer feedback and suggest improvements.
Recently, Chen and his team have been investigating machine learning algorithms by investigating notions of fairness. “Equity is a very hot topic right now,” she says. “In mathematics, there are different notions of fairness, but it is always a complex and evolving subject. »
And Chen’s advice to future AI developers? “Critical thinking will become even more important to understand the solutions to be proposed, to make judgments about your own innovations and to know whether the result generated is good or bad,” she says, reflecting on how this skill will become increasingly increasingly important for businesses. future AI developers.
Product management
Nadine Hoffmann, SAP Business AI, Germany
“I translate and I want to fascinate,” says Hoffmann, global AI product manager in the growth area of SAP Business AI.
Even after more than 20 years at SAP, disruptive ideas and mindset shifts continue to energize Hoffmann. Being an expert in new technologies, and being energized and excited by their constant volume and speed, is essential to succeed in today’s AI product management teams, because product management is the glue between partners, customers, the field, and development.
“On the one hand,” says Hoffmann, “SAP has data scientists, software engineers and researchers who take our software to the next level. And on the other, experts who define the legal and ethical safeguards.” Product management must master both the “technical language of software” and the “customer language” to understand desires, pain points and business processes.
Pivoting between these and aligning customer language with the technical language of the software is akin to being a translator, Hoffmann says. Our software will only meet customer requirements if there is a common understanding between the teams responsible for technology development, the legal teams responsible for ethical and legal compliance, and the customer.
Whatever the latest innovation, Hoffmann says that success in product management is not just about convincing teams and partners of the ease and positiveness of a technology, but also instilling in them a fascination with it- this so that they become passionate advocates and are intrinsically motivated to do so. learn more for themselves.
Data Science Engineering
Puntis Palazzolo, AI Strategist and Ethics Officer, SAP SuccessFactors, USA
“The ethical challenges posed by AI have transcended individual companies, extending beyond entities like SAP. It is crucial that we collaborate with others to collectively address emerging AI concerns,” says Palazzolo, who leads the SAP SuccessFactors Data Science team.
The team acts as a consulting service on AI use cases for product teams in SAP SuccessFactors, analyzing the problem, developing code and algorithms, and creating proofs of concept. Successful AI use cases are then integrated into SAP SuccessFactors solutions.
A large portion of the data in SAP SuccessFactors solutions is sensitive customer data. With the dramatic increase in generative AI use cases, protecting customer data must take priority, says Palazzolo. “Generative AI is a powerful technology that introduces new challenges, such as hallucinations and automated decision-making. In high-risk industries like HR, we need to explain how we make certain decisions, especially when we impact people’s lives.
Palazzolo joined SAP 11 years ago and has been based in Palo Alto, California since 2013, where she represents SAP on MLCommons – a collaboration between universities and companies, such as Google and NVIDIA, dedicated to developing safe practices and industry standard benchmarks for improving AI models.
His advice to current and potential AI practitioners? Follow your passion, be ethical, and make your voice heard while there is still time.
“Lawmakers cannot single-handedly write AI regulations for us because they do not fully understand the complexity,” she says. “We can’t solve all the problems on our own, but we must raise our voices to shape the future of AI. »
Alexa MacDonald is editor-in-chief of SAP News.
