Women in AI leverage tech to turn data into solutions
Artificial intelligence is a male-dominated space, but the narrative is changing. Three women in this field embraced data science and are defying skeptics to thrive. https://rb.gy/5adsm
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00:35 I'd describe myself as a problem solver.
00:39 I'm a person who just loves solving problems.
00:43 And I work as a data scientist at AIC,
00:45 which mostly involves just finding
00:47 solution to different problems, especially
00:49 in the African space.
00:51 So AIC stands for AI Center of Excellence.
00:54 And we have three things that we mainly do.
00:56 We do capacity building, which is training.
00:58 We also offer-- we create software and sell
01:01 the software that is mainly AI-based.
01:04 And then we also do AI as a service,
01:09 where a company maybe wants AI implemented.
01:12 And then they just come in.
01:13 We do solutions for them.
01:14 So where we are right now is Adenian Labs.
01:17 So there are different startups inside here.
01:20 So we do AI for also those startups.
01:22 So everything they need that is AI-related,
01:24 we'll be the ones to create that for them.
01:26 I am a data scientist in agriculture space.
01:31 And often, I do other fields or engage
01:34 in projects in other fields.
01:35 So yeah, I am interested in exploring other sectors
01:39 besides the one I primarily do.
01:42 Artificial intelligence is applied in agriculture
01:46 from--
01:47 you know, with a prediction.
01:49 Farmers to know when it's going to rain,
01:53 to help them decide when they are going to plant
01:55 or when they are going to do all the other activities that
01:59 are dependent on climate.
02:02 Secondly is predicting yield, especially
02:06 for commercial purposes.
02:10 You want to know how much you're going to make from your input
02:16 so that you actually can plan that, well,
02:19 I want this amount of output.
02:21 So I need this amount of input.
02:23 There's also disease and pest detection in crops.
02:29 So I can take a picture of my leaves or my crops.
02:32 And then from the model thing, it
02:35 can tell me that this crop actually is not well.
02:38 And this and this is what you can do.
02:40 Again, work hand-in-hand with an agronomist
02:42 to help you know what exactly to advise the farmer.
02:45 I am a chief of growth and operations
02:48 for Powerland Project, which is an impact organization working
02:53 to build capacity and technology across Africa.
02:56 We are based in five countries--
02:58 Kenya, Tanzania, Nigeria, South Africa, and Zambia.
03:03 Currently, we are training about 10,000 young people
03:06 in software development.
03:07 So we provide a scholarship to train in software programming
03:11 languages, as well as entrepreneurship
03:14 and soft skills.
03:16 And then we direct the young people
03:18 to three pathways, which include job creation, opportunities
03:23 for work, entrepreneurship, and also advancing
03:27 to higher level courses in software engineering.
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03:33 I will say I'm very lucky to work in an environment
03:40 where your opinion matters as much as your male counterparts
03:44 does.
03:45 But I will not lie and say that I have not walked into a room
03:49 and said what I do and felt like people did not believe me
03:51 until I actually showed them.
03:54 Sometimes you just talk to people,
03:55 and maybe it's a client, and they want you to do something.
03:59 But then you just feel like you have to prove yourself,
04:03 because they just keep asking you these questions that they
04:07 are not asking their male counterparts.
04:09 Sometimes they'll walk into a room, and you're seated there.
04:12 But then you won't be asked the complex questions.
04:14 So they'll ask the males in the room.
04:18 So it's just I've learned with time.
04:21 Like, if I feel like I'm being sidelined,
04:23 I have to speak up.
04:24 Because you can't blame people.
04:26 That's how most of us have been programmed to think.
04:29 Like, we think the women in the room
04:31 cannot possibly be as good.
04:33 So sometimes you just have to speak up,
04:35 and then that person will be like, OK, yeah.
04:37 So this person can also lose a lot.
04:39 Yeah.
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04:43 The benefit of understanding a woman's body
04:49 from their biological software aided by AI
04:54 to help women understand their monthly periods, trends,
04:58 something like that, they're able to predict
05:01 if there's any anomaly.
05:03 They're told, actually, the way your months are going,
05:05 probably you need to seek help.
05:08 There's breast cancer.
05:09 There's these cancers going on.
05:11 And with that, AI can help women understand,
05:15 be able to take care of themselves health-wise.
05:19 [MUSIC PLAYING]
05:22 So there are a couple of policies
05:31 that have been implemented, both globally and even here
05:34 in Kenya.
05:36 But I think we still have quite a long way to go.
05:41 And why I say this is because technology is very dynamic.
05:43 So it evolves every day.
05:44 There's something new.
05:46 So whatever policies we set-- we know the period to make policies
05:49 takes a while.
05:50 So whatever policies we set before, ideally,
05:53 are not the same we can use for the new emerging technologies.
05:57 But I think, for one, is just looking
05:59 at organizations such as the United Nations
06:02 and how they've come across to say,
06:05 these are some of the challenges.
06:07 These are some of how we can reduce some of these biases,
06:10 like what I was talking about around gender equality.
06:14 And even just to give the capacity building
06:18 to some of these organizations that are using AI,
06:20 that are implementing AI, so that they
06:22 can be aware of these challenges as they're
06:24 designing solutions for them.
06:26 I know one particular policy that
06:31 is very close to my heart, per se,
06:34 is around creating equality when it comes to recruitment.
06:38 So looking around at how AI can support
06:42 in terms of the recruitment process,
06:45 where it doesn't have biases to certain--
06:48 so if there's an opportunity for work,
06:50 there's no bias to whether you are a man or a woman.
06:53 Because you just look at the skill set.
06:55 So there's some policies around human resource
06:58 that have been implemented that I think are going
07:01 to help us to look for, because they reduce some of the biases
07:04 that we have naturally.
07:05 [MUSIC PLAYING]
07:09 Yes, statistically speaking, I won't lie.
07:15 There's a statistical advantage of men
07:18 being dominating the tech field, which cuts across all the tech
07:23 fields.
07:24 But then again, I would also say there
07:27 is a systematic bias with that idea
07:30 also, because in terms of capabilities,
07:33 I think I've worked with women.
07:36 And I know what they can do and what they offer to the table.
07:41 So I would advise women to join, because there's
07:44 no specific thing that a man can do in tech that a woman can't.
07:48 It's just something of a--
07:51 it's just a systematic bias, a bias that exists,
07:54 a bias that needs to be ruled out.
08:00 This is a conversation that needs to be changed.
08:03 I've worked with women, and I would
08:04 advise more women to join AI, because it's an evolving field.
08:11 And it's an evolving field that requires everyone.
08:14 It requires the input of everyone,
08:17 people across all genders, people across all religions,
08:20 people across-- because it's here with us,
08:22 and it's something that we all have to embrace.
08:24 So I would advise women to join.
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08:45 (upbeat music)
08:48 you